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THE ROLE OF SATELLITES IN WMO PROGRAMMES IN THE
2010s
2003
SP-1
TECHNICAL DOCUMENT
WMO/TD No. 1177
TABLE OF CONTENTS
Page
FOREWORD
EXECUTIVE SUMMARY ................................................................................................................. I
CHAPTER 1
HISTORY AND DEVELOPMENT OF THE SPACE-BASED PORTION OF THE GLOBAL
OBSERVING SYSTEM ................................................................................................................ 1-1
Meteorological Satellites and WMO Programmes ........................................................................ 1-1
Rationale for the Technical Document ......................................................................................... 1-4
Historical Review of the Global Observing System ....................................................................... 1-4
Present system ............................................................................................................................ 1-9
References: ............................................................................................................................... 1-11
CHAPTER 2
CURRENT CAPABILITIES AND WMO OBSERVATIONAL REQUIREMENTS .......................... 2-1
References: ................................................................................................................................. 2-5
CHAPTER 3
CHALLENGES FOR THE OBSERVING SYSTEMS .................................................................... 3-1
Meeting remote sensing requirements ......................................................................................... 3-1
Improving Understanding of the Earth System, its Components and their Interactions ................. 3-2
CHAPTER 4
NEAR-TERM CONFIGURATION OF THE SPACE-BASED COMPONENT OF THE GLOBAL
OBSERVING SYSTEM (GOS)..................................................................................................... 4-1
Current and Future Polar platforms .............................................................................................. 4-2
Current and Future Geostationary Platforms ................................................................................ 4-6
Enhanced monitoring of selected Earth system components ....................................................... 4-7
Data handling and communications ........................................................................................... 4-10
CHAPTER 5
VISION FOR THE FUTURE: A 2020 PERSPECTIVE................................................................. 5-1
Satellite System Characteristics ................................................................................................... 5-2
Geostationary Satellites ............................................................................................................... 5-3
Low Earth Orbit Satellites............................................................................................................. 5-4
Challenges for the Future............................................................................................................. 5-5
ANNEX I
PHYSICAL
BASIS
OF
REMOTE
METHODS
OF
DETERMINING
HYDROMETEOROLOGICAL AND GEOPHYSICAL PARAMETERS
ANNEX II
OBSERVATIONAL REQUIREMENTS FOR WMO PROGRAMMES
ANNEX III
PROCESSING, INTERPRETATION, DISSEMINATION AND STORAGE OF
SATELLITE DATA
ANNEX IV
NEAR TERM CONFIGURATION OF THE SPACE BASED COMPONENT OF THE
GLOBAL OBSERVING SYSTEM
FOREWORD
In the 1970s, the space-based component of the Global Observing System (GOS) of
WMO’s World Weather Watch experienced a major expansion. Meteorological satellites - the first
one was launched in 1960 - were starting to provide data, products and services on a global
operational basis from both geostationary and polar orbits. At the invitation of the World
Meteorological Organization (WMO), two visionary scientists, Dr D. Johnson (USA) and Dr I. Vetlov
(former USSR), prepared a technical document for WMO Members. The document not only
highlighted the potential contributions of meteorological satellites to all WMO Programmes, but
also proposed a future configuration for the space-based component of the GOS.
The proposed configuration was finally realized in the mid-1990s. Over the period,
especially since 1977, marked advances in remote-sensing and in computer capabilities,
communications and space-based technology had revolutionised our ability to observe the Earth
and its atmosphere from space. In addition the number of space-faring nations has grown. These
developments further accelerated and extended the global monitoring and applications capabilities
to domains beyond weather. As a result, the expectations and requirements of the world
community from WMO Programmes grew considerably, especially in a number of areas of concern
to humanity related to sustainable development such as climate change and its impacts, the
negative impacts of natural disasters, dwindling water resources, monitoring of oceans, land and
ecosytems, increased pollution and depletion of the ozone layer.
Over the next 25 years, the anticipated advancements in space-based observing systems,
computers, data handling and information and communications technology are expected to further
eclipse the recent achievements. In particular, we should expect considerable improvements to the
global space-based observing system based, among others, on very high spatial and spectral
sampling from ultraviolet to microwave wavelengths, with both active and passive sensors. By
2025, and likely before then, operational and research satellite systems from a variety of orbits will
provide a spectral shell of real- and near-real time information that will be used to address societal
and environmental problems in an increasing number of application areas. However, over a shorter
time scale, especially in only the first decade of this 21st century, we must prepare ourselves to
develop new approaches to data handling and applications through international partnerships. This
is essential if the international community is to benefit fully from the growth, of several orders of
magnitude, in the volume and content of satellite data which will be available from systems!
In recognition of these developments and the corresponding challenges and opportunities
to WMO and the National Meteorological and Hydrological Services (NMHSs), the Organization
has again felt the need to share and reflect on the potential contributions from present and future
satellite systems. In this regard, WMO is indeed fortunate to receive the contributions of three
eminent scientists, namely, Dr Ghassem Asrar, Dr Tillmann Mohr and Mr Greg Withee. In this
technical document, they have provided their considerable expertise in highlighting the new and
exciting potentials for observations from space. They have also proposed their vision of the future
space-based component of the GOS. This document offers a view towards identifying the best
approach in achieving and managing these developments, especially in the context of the new
technological advances and environmental concerns. In the 2025 timeframe, WMO Members will
certainly regard this document as having been a guiding light in expanding upon the challenges
articulated over 25 years ago. I am therefore thankful to the three authors for this valuable
contribution to WMO’s Global Observing System in support of the NMHSs of Members, the
Programme and activities of WMO and in addressing the challenges facing humanity over the next
decades.
(G.O.P.Obasi)
Secretary-General
EXECUTIVE SUMMARY
This document, a WMO publication on “The Role of Satellites in WMO programmes in the
2010s” is intended to update the last comparable publication entitled: “The Role of Satellites in
WMO programmes in the 1980s” by D.S. Johnson and I.P. Vetlov published in 1977. This update
was prepared by three primary authors: Dr G. Asrar, Dr T. Mohr and Mr G. Withee, with assistance
from additional experts as identified and recruited by the primary authors. WMO Members
involved in the Consultative Meetings on High-Level Policy on Satellite Matters felt strongly that the
new publication would be of great importance to WMO Members, not only to the NMHSs but also
the larger communities among the Members. For example, such users would include policy
decision-makers or those involved with the IPCC assessment process. It is envisioned that there
will be widespread use of the new publication by many user communities as nations progress into
the new century and prepare for a new set of societal and environmental challenges across the
globe.
Over the last four decades, since the launch of TIROS-1, satellite data have progressed
from the era of simply “pictures” to high resolution digital renderings from a variety of spectral
bands that provide both qualitative and quantitative information about the atmosphere, clouds, and
land and sea surface properties. Today, environmental satellites are used for a variety of
applications that span scales from now casting to climate, and include land, ocean, atmosphere
and ecological applications. It is well recognized that meteorological satellites provide essential
data for weather forecasting to national weather services across the globe; indeed it would be
difficult to find an area of operational meteorology that had not come under satellite influence.
Simultaneously, new instruments on research satellites are providing insights into future satellite
systems to the extent that a variety of environmental applications are growing vigorously. The
science and applications that develop from space-based observing systems will have far reaching
impacts on WMO Members and programmes. This document focuses on the role of environmental
satellites as part of a Global Observing System (GOS) as we rise to meet the challenges of the 21st
century.
The document is divided into five Chapters with supporting Annexes. Chapter 1
addresses Meteorological Satellites and WMO programmes, and traces the history and
development of the space-based portion of the GOS from its inception to where it is today.
Chapter 2 presents the reader with a synopsis of current capabilities and how they meet WMO
observational requirements for the Global Observing System and WMO supported programmes. It
then provides a brief description of why they are important, trends for possible future requirements,
and how these requirements are developed, reviewed, and assessed. Chapter 3 addresses the
role of future technology for satellite systems in meeting the science challenges of both today and
20 years hence, and looks to observing system technology, from both operational and
experimental satellites, that is expected to become available during that time period. Chapter 4
addresses the future evolution and challenges of the space-based portion of the GOS to meet the
needs of WMO programmes. Annex I addresses the science of remote sensing. Annex II
presents Observational requirements for WMO programmes. Annex III deals with processing,
dissemination and storage of satellite data and how it will evolve to a data and information delivery
system. Annex IV provides information regarding the near-term improvements expected in the
space-based component of the GOS.
While Johnson and Vetlov focused on the potential contributions by operational
meteorological satellites, this publication covers the spectrum of operational and experimental
environmental satellites. The tremendous impact experimental satellites have had in providing
new data and products in support of the WMO and WMO supported programmes is profound,
wide-spread, and continues to increase. During the next few decades, the integration of
experimental satellites with the evolving operational satellite systems will lead to a comprehensive
space-based observing system that will have the potential of meeting many of the needs of WMO
and WMO supported programmes. Through integration and exploitation of the strengths of each
satellite system, maximum utilization can be realized.
- ii The international realization of new environmental data through satellite space and ground
systems brought to all members of the WMO, when combined with selected quality Earth-based
observations, bode well for helping WMO users to be prepared to answer the pressing
environmental and societal questions of tomorrow.
(Authors Signatures here)
CHAPTER 1
HISTORY AND DEVELOPMENT OF THE SPACE-BASED PORTION OF THE GLOBAL
OBSERVING SYSTEM
Meteorological Satellites and WMO Programmes
Barely 40 years after the launch of the first meteorological satellite in 1960, it is difficult to
find any WMO programme or project that does not use or depend on satellite data. Uses of
satellite data include training, operational meteorology, climate studies and modelling, agrometeorology and oceanography. During these first decades of the 21st Century, WMO Members
will continue to exploit operational meteorological satellite systems, while also expanding utilization
to include experimental satellites1. During the next decades existing capabilities will be refined
and improved, while new applications and advanced technology will migrate from the experimental
realm to full operational use.
The potential for satellites to contribute to WMO programmes is truly remarkable, with
observations that have the potential to satisfy many of the WMO requirements. As can be seen
throughout this technical document, satellites provide a wide variety of essential data for use by
WMO Members. For the atmosphere they measure cloud cover, temperature and humidity from
the surface of the Earth to over 40 km in altitude, winds, precipitable water, precipitation, as well as
ozone and other trace gases. They also observe aerosols, dust and volcanic ash. Over the
oceans they measure surface temperature and winds, ocean levels and waves, ice cover and seasurface biology. Over land they observe vegetation and snow cover, floods, forest fires and many
other parameters, including monitoring of crops and drought conditions. Additional products are
designed specifically for monitoring global climate and detecting climate change.
Satellites provide data that complement in situ observing systems and, in many cases, are
the only source for observations. They provide global data coverage on a timely basis (Figure 1),
which is only one of their advantages. There are few conventional observations from the world's
oceans, which cover some 70% of the planet. Additionally, few observations are available from
deserts, forests, polar regions or other sparsely inhabited parts of the planet. Global data are
essential for many disciplines, and satellites offer the only practical solution to meeting the
requirement for global coverage. In many cases measurements by satellites are made with
remarkable accuracy: surface winds over the oceans can be measured with accuracy comparable
to that of ship observations, ocean levels can be determined with an accuracy of a few
centimetres, the temperature of any part of the atmosphere, anywhere in the world can be
measured with accuracies that are useful inputs to numerical models. In many cases these levels
of performance have been reached with first or second-generation instruments; even greater
precision and usefulness is expected from instruments on future satellites.
These remarkable capabilities call for constant algorithm review, development and
innovations. Some capabilities still require further improvements in order to meet the demanding
and stringent WMO requirements for accuracy, coverage, resolution or timeliness. It is important
to realize that satellite data are totally different in character from in situ data and have to be used in
ways that reflect their characteristics. This means adapting the application methodology to suit the
data, rather than the reverse. For example, atmospheric models are now adapted to use the
radiances directly, instead of inverting satellite radiances into atmospheric temperatures as if they
came from balloons.
1
For this document the definition of experimental satellites is defined in the 1993 edition of the “Manual
for the Global Observing System (WMO Publication No. 544):
“An environmental observation satellite with the primary purpose of acquiring a defined set of research data;
testing new instrumentation and/or improving existing sensors and satellite systems; and/or it may provide
information for operational use, but has limitations due to the lack of a commitment to ensure continuity of
service or a reliable satellite replacement policy; and also due to non-consistent modes of operations.”
1-2
Figure 1 - Composite image obtained from the global system of operational meteorological
satellites in polar and geostationary orbit. It shows global cloud cover, sea surface
temperatures, land surface temperatures and ice cover. Global images such as these are
generated on a routine basis, and are updated every three hours.
The flight of pre-operational or demonstration satellite missions provides the opportunity to
evaluate data in a research environment before they are placed into full operational use, but it is
important to recognize that new techniques and completely new applications are often discovered
only after many years of operational use. New uses for cloud imagery are still being developed
after four decades of routine use.
The ability of geostationary satellites to provide a continuous view of weather systems
makes them invaluable in following the motion, development, and decay of extreme meteorological
phenomena. For example, short-term events such as severe thunderstorms, with a life-time of only
a few hours, can be successfully recognized in their early stages of development and appropriate
warnings of the time and area of their maximum impact can be provided to the general public.
Hurricanes, typhoons and tropical storms can be monitored continuously, providing important
information on their location and movement, placing the geostationary satellite at the heart of the
warning process. Derivation of atmospheric winds from the analysis of successive satellite images
has become an important product especially for NWP. Thus, the capability to provide timely
information to improve warnings has been the primary justification for the geostationary spacecraft.
Flying in a near-polar sun-synchronous orbit, the polar orbiting spacecraft provides both day
and night coverage of the entire globe once every 24 hours. The polar-orbiting satellites are
principally used to obtain: (a) daily global cloud cover; (b) accurate quantitative measurements of
surface temperature; and, (c) information on the vertical profile of atmospheric temperature and
water vapour for use in a variety of application areas, most notably numerical weather prediction.
1-3
Together, the polar-orbiting and geostationary satellites constitute a global meteorological satellite
network, providing valuable information to WMO Members for a variety of applications, including
global and regional NWP.
A particular advantage of the polar orbiting satellite is that one instrument can observe the
entire planet once or twice within a period of 24 hours. This has the important benefit that there
are no inter-regional calibration errors. Satellite instruments are extremely sensitive and are able
to detect and measure reflected or thermal radiation from an altitude of around 800 km for polar
and 36,000 km for geostationary orbit. Instrument calibration stability over time is carefully
monitored in order to provide continuity between instruments on successive satellites. This is of
crucial importance for climate measurements.
Figure 2 - Distribution of user stations of WMO Member States showing more than 1,300
stations located in 125 countries.
Although all satellite data must be used with due caution and proper regard for what they
represent, this does not alter the fact that these data have become essential tools for many
programmes. This offers encouragement to the development of further improvements over the
coming decades, many of which are described in other chapters of this document.
The thrust of the current generation of meteorological satellites is aimed primarily at
characterizing the kinematics and dynamics of the atmosphere. The ability to achieve such
objectives was demonstrated during the Global Weather Experiment in 1979. This capability is
now part of the global operations of the World Weather Watch. The existing network of
meteorological satellites, which form part of the Global Observing System (GOS) of the World
Weather Watch, produces real-time weather information on a regular basis. Today’s operational
meteorological satellites have comprehensive data broadcast capabilities.
Virtually every
meteorological centre in the world has at least one receiver for the direct reception of satellite data
(Figure 2). With those facilities, transmissions can be received directly from the nearest
geostationary satellites, providing quasi-continuous cloud imagery. These transmissions often
include other types of meteorological data as well as imagery from other satellites. Operational
polar satellites also broadcast data continuously as they orbit the Earth, providing local highresolution data as they pass within view of the receiving. Reception of data from experimental
satellite missions have generally been restricted to one central ground station and a few licensed
sites, but increasing amounts of processed data are being made available over the WMO’s Global
Telecommunications System (GTS) and over the Internet. This is a welcome development, but
there is certainly room for further improvements in early utilization of new forms of data. One such
1-4
improvement has been instituted by NASA, through the direct broadcast of high resolution digital
image data from MODIS, along with the provision of an applications processing package for data
users; however, those data are transmitted at X-band frequencies. The cost of such receiving and
processing ground stations is considerably more than that for the current operational
meteorological satellites stations receiving data transmitted in S-band.
For most WMO programmes data continuity is essential. Increasing reliance on satellite
data for operational applications means that gaps in the supply, due to a failed satellite for
instance, could have very serious impacts. Continuity of observations for climate monitoring is also
of great importance. In many cases great efforts have been made to ensure continuity through
contingency plans and in-orbit spare satellites, but this is not invariably the case. Furthermore,
research satellite data are playing increasingly important roles within the global observing system:
this is evident in the area of ocean surface wind and height observations, as well as planned uses
of high spectral resolution data for NWP applications. Continuity of operational satellite data is a
must, pathways from research to operational satellite systems is most desirable.
Rationale for the Technical Document
In 1977, a prophetic report, The Role of Satellites in WMO Programmes in the 1980s by
D.S. Johnson and I.P. Vetlov [5], described the future space-based component of the Global
Observing System. Through the efforts and commitments of a few space-faring nations and
organizations, Johnson and Vetlov’s vision was realized in the mid-1990s. Since 1977, the radical
growth in computer capability, communications and space- based technology has revolutionized
our ability to observe the Earth and its atmosphere from space. Continued growth in these basic
technological areas during the new millennium make the outlook for even greater advances and
capabilities achievable through space based remote sensing enormously positive. This technical
document reflects on past achievements and looks to the future space-based component of the
Global Observing System. The horizon for this technical document is the first two decades of the
21st Century: a new era in which WMO Members will continue to exploit operational meteorological
satellite systems, while expanding their capabilities by taking advantage of information provided by
experimental satellite systems.
While Johnson and Vetlov focused on the potential contributions of operational
meteorological satellites, this technical document will address the spectrum of operational and
experimental environmental satellites. The tremendous impact experimental satellites have made
in providing new data and products in support of WMO and WMO supported programmes is wide
spread, profound and continues to increase. During the next few decades, the integration of
selected experimental satellites with the evolving operational satellite systems will lead to a
comprehensive space-based observing system that will have the potential for meeting many of the
needs of all WMO and WMO supported programmes. Through integration and exploitation of the
strengths of each satellite system, it will be possible to achieve maximum utilization. Such
exploitation bodes well in helping today’s users be prepared to answer the pressing environmental
and societal questions of tomorrow.
Historical Review of the Global Observing System
Since the inception of the World Weather Watch (WWW), the space-based component of
the Global Observing System (GOS) has had a profound impact on WMO Members. Its
observational capabilities have led to data and information that permeate almost all products and
forecasts provided by the National Meteorological and Hydrological Services (NMHS). Indeed, as
has been pointed out by WMO’s Secretary General, Professor G.O.P. Obasi, meteorological
satellites provide essential data for weather forecasting to NMHSs across the globe. Indeed, the
space-based component of the GOS marked the genesis of the WWW. The WWW brings new
and exciting possibilities for observations from space. It serves as the catalyst, to bring together
some of the world’s leading experts to provide a blueprint for international cooperation that is
unparalleled within the meteorological communities. The vision and genius of such a small select
1-5
group has produced reverberations across the world that have stood the test of time and foretold
the role of satellites in the global observing system.
WMO was formally established on 23 March 1950. At that time, and for nearly the next
two decades, there were still large areas of the Earth’s surface without adequate observational
systems [6]. All this was soon to change in a profound and explosive fashion with the beginning of
the space age.
The then USSR launched the first Earth-orbiting satellite, Sputnik-1, on 4 October 1957.
The USA launched its first satellite, Explorer I, on 2 January 1958. First light at the new day’s
dawn for meteorology occurred with the launch of TIROS-1 on 1 April 1960. TIROS-1 was a low
Earth orbiting satellite with a television camera that provided the first pictures from space of the
Earth, revealing the distribution and the complexity of its clouds and cloud systems. The potential
for such a new observing system was immediately obvious.
On 20 December 1961 the United Nations General Assembly (UNGA) unanimously
passed Resolution 1721 (XVI) on the “International Co-operation in the Peaceful Uses of Outer
Space”. The UNGA noted the exceptional potential afforded to meteorological science and
technology by observations of the Earth from outer space and was convinced of the worldwide
benefits to be derived from international cooperation in weather research and analysis. In the light
of developments in outer space, it recommended to all United Nations Member States, to WMO
and other appropriate specialized agencies the early and comprehensive study of measures: to
advance the state of atmospheric science and technology in order to provide greater knowledge of
basic physical forces affecting climate and the possibility of large-scale weather modification; to
develop existing weather forecasting capabilities and to help UN Member States make effective
use of such capabilities through regional meteorological centres [1]. The resolution further
requested WMO, in consultation, as appropriate, with the United Nations Educational, Scientific
and Cultural Organization (UNESCO) and other specialized agencies, governmental and nongovernmental organizations, such as the International Council of Scientific Unions (ICSU), to
submit a report to WMO Members and to the United Nations (UN). This report would become the
bellwether for WMO. The UNGA requested the report for earliest consideration six months later in
June 1962!
WMO quickly agreed upon a course of action to meet such a demanding deadline. Two
world recognized leaders in the young science of satellite meteorology, Dr H. Wexler from the USA
and Academician V.A. Bugaev from the USSR worked together in Geneva, Switzerland to prepare
the First Report of the WMO on the Advancement of Atmospheric Sciences and Their Application
in the Light of Developments in Outer Space [1]. Eventually, there would be four more such
reports but the first was to have the greatest impact on WMO Members. Sir Arthur Davies, the
Secretary-General of WMO, at the time declared, “The First Report undoubtedly ranks as one of
the major documents in the history of WMO” [6].
Wexler and Bugaev highlighted potential benefits of satellite data to both operational and
research communities. Having laid the foundation, they then proposed a new structure, the WWW.
The WWW was established in response to the emergence of the meteorological satellite and was
based on an observing system comprised of meteorological satellites and conventional
observations. Wexler and Bugaev went further to describe, in general terms, the space-based
component of a global observing system (Figure 3). One of the new observing system’s main
requirements was the continuous existence of one or more satellites [1]: at the time of the First
Report only polar-orbiting satellites existed and the space-based component of the GOS needed
only one or more such satellites.
1-6
Figure 3 - The first space-based component of the Global Observing System, 1961
In the course of the evolution of the space-based component of the GOS, a slow and
steady increase in scope has occurred and this evolution has been guided by user requirements,
compelled by technological advancements and made possible by the few space-faring nations and
organizations in the world.
The Fourth WMO Congress, in April 1963, endorsed the First Report with appreciation, in
particular with respect to the concept of the WWW. Thus, WMO established the WWW in less than
two years after the first request from the UNGA. In 1966, the Fifth Report of the WMO on the
Advancement of Atmospheric Sciences and Their Application in the Light of Developments in
Outer Space, WWW – Phase II, August 1966 [2] formally refers to the GOS for the first time:
“It follows that the essential elements of the WWW are: the observational networks and other
observational facilities, hereafter called the Global Observing System;” [2]
In 1966, a technology demonstration communications satellite, ATS-1, flew in
geostationary orbit with a meteorological payload, the spin scan cloud camera (SSCC). Repeat
imaging from ATS’ SSCC allowed meteorologists for the first time to monitor continuously cloud
and weather system development. Guided by the acknowledged father of satellite meteorology, Dr
V. Suomi - winner of the WMO IMO award in 1993 for his leadership in this field - the geostationary
satellite emerged as a vital and necessary component of the GOS. The WMO, WWW, The Plan
and Implementation Programme, 1972 – 1975, July 1971 [3] contained a new and more formal
description of the space-based component of the GOS:
1-7
Meteorological satellites can be divided into two groups, those in polar or near-polar orbits
and those in geostationary orbit. With the former it is possible to choose the orbit altitude within a
wide range, while with the latter it must be approximately 36,000 km. A satellite in near-polar orbit
at an altitude of, approximately 1,000 km, has a big advantage over a geostationary satellite as
regards the resolution that can be obtained with a particular optical system. The near-polar
orbiting satellite has the further advantage of being able to observe the whole globe whereas a
geostationary satellite can only provide useful cloud cover information in an area within a range of
about 60-65 degrees from the sub-satellite point. In contrast, geostationary satellites are able to
provide much higher temporal coverage of the surface.
Figure 4 - The 1978 space-based component of the Global Observing System
During the 1970s (Figure 4), satellite operators were steadfastly implementing a spacebased component of the Global Observing System. The WMO, WWW, Third Status Report on
Implementation, July 1970 [4] described the European Space Research Organization (ESRO)
project to launch a geostationary satellite in 1977 over approximately the 0° longitude, and also the
Japanese plan to establish a geostationary meteorological satellite (GMS) at 120°E as part of its
contribution to WWW and the Tropical Cyclone Project [4]. The WMO, WWW, Sixth Status Report
on Implementation, July 1973 [7] noted that the USA was developing a Geostationary Operational
Environmental Satellite (GOES) service to be inaugurated late in 1973. The Sixth Status Report
also recorded that the USSR had announced its plans for setting up a geostationary meteorological
satellite at approximately 70°E longitude.
1-8
By 1977, the space-based GOS was well established in terms of plans and
implementation. However, the pace of development and implementation had not kept abreast of
the rapid advances being made in space based observing technology. The introductory chapter for
the WMO, WWW, Planning Report No. 36, The Role of Satellites in WMO Programmes in the
1980s, 1977 by D.S. Johnson and I.P. Vetlov [5] heralded an impending change of major
significance.
“Until quite recently this early planning for WWW served as a useful guide for the development of a
global satellite observing system. However, satellites will soon play a far greater role than was
originally anticipated in 1961. Satellites will play an increasingly important role not only in obtaining
observational data, but also in providing a capacity for the collection and distribution of information
in support of various WMO programmes.” [5]
The defining phrase “in support of various WMO programmes” greatly extended the scope
of responsibility for the space-based component of the GOS. Not only the WWW but also almost
all WMO programmes would be served. This expansion would require a review of the definition at
the time of the space-based GOS. Johnson and Vetlov would propose a possible constellation of
satellites consisting of three to four satellites in quasi-polar orbits and four to five geostationary
satellites (Figure 5).
Johnson and Vetlov alerted the meteorological community to the potential for a rapid
increase in new data sets, by listing a number of expected parameters for the atmosphere, clouds,
ocean surface and land surface. Thus, the technology pillar was again urging user requirements to
be responsive to new capabilities. This urging would culminate in a concerted effort during the
1980’s of the WMO Technical Commission to define its user requirements.
1-9
Figure 5 - The nominal configuration of the space-based component of the Global
Observing System in the 1990s
Present system
The evolution to today’s constellations of satellites that comprise the space-based portion
of the GOS fulfils the vision of Johnson and Vetlov (Figure 6). The geostationary satellite
constellation operates in the equatorial belt and provides a continuous view of the weather from
roughly 70N to 70S. At present there are satellites at 0 longitude and 63E (operated by the
European Organisation for the Exploitation of Meteorological Satellites - EUMETSAT), 76E
(operated by the Russian Federation), 105E (operated by the People's Republic of China), 140E
(operated by Japan), and 135W and 75W (operated by the United States of America (USA)).
The second, and oldest constellation is comprised of polar-orbiting satellites operated by the
Russian Federation, the USA and the People’s Republic of China. The METEOR-3 series has
been operated by the Russian Federation since l991. The polar satellites operated by the USA are
an evolutionary development of the TIROS satellite, first launched in April l960. The present
NOAA series, based on the TIROS-N system, has been operated by the USA since l978. FY-1C,
the third in the series of China’s polar-orbiting satellites, is now operational. From altitudes of 850
to 900 km, these spacecraft provide coverage of the polar regions that are beyond the view of the
geostationary satellites.
1-10
Figure 6 – The present configuration for the space-based component of the Global
Observing System
NOTE: Information on the characteristics, capabilities and uses of the current system of
meteorological satellites is contained in the CGMS Directory of Meteorological Satellite
Applications. Additional up-to-date information can be found via the WMO Satellite Activities
Homepage: http://www.wmo.ch/hinsman/satsun.html. Information on Meteorological and Other
Environmental Satellites (WMO-Pub No. 411) contains further relevant information.
1-11
References:
[1]
[2]
[3]
[4]
[5]
[6]
[7]
First Report of the WMO on the Advancement of Atmospheric Sciences and Their
Application in the Light of Developments in Outer Space
Fifth Report of the WMO on the Advancement of Atmospheric Sciences and Their
Application in the Light of Developments in Outer Space, WWW – Phase II, August 1966
WMO, WWW, The Plan and Implementation Programme, 1972 – 1975, July 1971
WMO, WWW, Third Status Report on Implementation, July 1970
WMO, WWW, Planning Report No. 36, The Role of Satellites in WMO Programmes in the
1980S, 1977 by D.S. Johnson and I.P. Vetlov
Forty Years of Progress and Achievement, A Historical Review of WMO, Edited by Sir Arthur
Davies, Secretary-General Emeritus, WMO, 1990
WMO, WWW, Sixth Status Report on Implementation, July 1973
CHAPTER 2
CURRENT CAPABILITIES AND WMO OBSERVATIONAL REQUIREMENTS
In the past forty years, remote sensing capabilities on both polar and geostationary
platforms have been established. Those capabilities have proven useful in monitoring and
predicting severe weather such as tornadic storm outbreaks, tropical cyclones, and flash floods as
well as climate trends indicated by sea surface temperatures, biomass burning, and cloud cover
(Figures 2.1 through 2.5). This occurred first with the visible and infrared window imagery of the
1970s, was augmented with the temperature and moisture sounding capability in the 1980s, and
further enhanced with microwave imaging and sounding capability in the late 1980s and 1990s.
Figure 2.1 - Total Column precipitable water
for January 1995 derived from three different
observing systems as part of NASA’s Water
Vapour Project. (NVAP)
Figure 2.2 - Instantaneous rainfall rate in a
cold-front approach NW Spain, derived from
Meteosat imagery. convective rainfall can
also be seen over southern Italy. (INM)
Figure 2.3 - Map of global Sea Surface Temperatures for 1 May 2001 derived from NOAA
Polar Orbiting satellites. (CIMSS)
2-2
Satellite imagery, especially the time continuous observations from geostationary
instruments, dramatically enhanced our ability to observe and measure atmospheric cloud motions
and to monitor hurricanes and severe thunderstorms. Geostationary depiction of temporal and
spatial changes in atmospheric moisture and stability are improving severe storm warnings.
Atmospheric flow fields (wind field composites from cloud and water vapour drift) are helping to
increase the accuracy of hurricane trajectory forecasting.
Figures 2.4 a and b - Sea Surface Temperatures in the Black Sea (Planeta). Figure 2.4 a (on left)
shows the conditions near the start of winter, 29 October 2000 and Figure 2.4 b (on right) shows
the situation near the end of winter, on 6 March 2001
Figure 2.5 - Composite image showing ERS Scatterometer winds as red vectors together
with 10 metre winds (in blue) from the HIRLAM NWP model. It can be seen that in some
areas the satellite data confirms the model analysis, in others the satellite winds add new
information. The wind fields are superimposed on a Meteosat cloud image (CMS)
Applications of these data also extend to the climate programmes; archives from the last
twenty years offer important information about the effects of aerosols and greenhouse gases and
possible trends in global temperature. Satellite sounder data fill important data voids at synoptic
2-3
scales. Applications include use of radiance data by NWP, temperature and moisture analyses for
weather prediction, analysis of atmospheric stability, estimation of tropical cyclone intensity and
position, and global analyses of clouds. Polar orbiting microwave measurements help to alleviate
the influence of clouds for all weather soundings.
WMO has a long history in defining its observational data requirements. WMO follows a
process that results in a hierarchical set of requirements. At the highest level, its Long-term
Planning Process guides the WMO. The Fifth Long-term Plan [2] is the current plan and spans the
time frame 2000 to 2009. The Plan provides a vision for the twenty-first century and contains
overall guidance, objectives, opportunities and challenges for the organization including those for
economic development, political development, demographic dynamics, urban environment, human
health, energy production and consumption, fresh water, land use, food security and combating
desertification, protection of climate and atmosphere, and natural and human-caused disasters.
Within each major WMO Programme, i.e., World Weather Watch Programme, World Climate
Programme, Atmospheric Research and Environment Programme, Applications of Meteorology
Programme, Hydrology and Water Resources Programme, Education and Training Programme
and the Technical Cooperation Programme, there are similar but focused high-level requirements
with guidance, objectives, opportunities and challenges. Within the various Programmes, there are
Technical Commissions assigned responsibility for a specific area, for example, the Commission
for Hydrology. In the nearer term, the four year Programme and Budget for WMO contains
guidance, objectives, opportunities and challenges that are based on the long-term objectives.
The work of the Technical Commissions is guided by the WMO Congress approved Long-term
Plan and Programme and Budget. The Long-term Plan is reviewed and updated in a four-year
cycle. Within the Technical Commissions as well as the various WMO supported programmes,
requirements are derived in order to satisfy the long-term goals. These requirements are
manifested in a statement of needs for various systems including observations,
telecommunications and data processing.
The current procedure for setting, reviewing and updating observational data
requirements for the various Technical Commissions is found in a process called the Rolling
Review of Requirements (RRR). All WMO Programmes and supported programmes have
ascribed to the RRR process, and it has become an effective tool to assess current capabilities of
a global observing system and provide guidance for future enhancements. Requirements versus
expected observing system performance are periodically reviewed and updated, normally on an
annual basis.
The RRR procedure consists of four stages:
(1)
a review of user requirements for observations, within areas of applications covered
by WMO programmes;
(2)
a review of the observing capabilities of existing and planned satellite and in situ
systems;
(3)
a "Critical Review" of the extent to which the capabilities (2) meet the requirements:
(a) both in situ and satellite observing systems are addressed;
(b) gaps and overlaps in existing and planned observing system capabilities, are
identified along with indications of the user requirements satisfied by these
systems;
(c) user requirements are addressed in an application driven way giving little
consideration to measurement characteristics, observing platforms, or data
processing systems;
(d) a database compendium of WMO user requirements and observing system
capabilities is generated. The resulting database allows a static view of the
2-4
observational requirements while the process allows dynamic review and
update, as appropriate; and
(e) cost benefit considerations may be developed, however, costs are not
included in the review process.
(4)
Development of a "Statement of Guidance" (SOG) based on (3) above.
The SOG relies on interpretation and analysis by observing system and applications
experts. It is guided by the critical review of the database of user requirements compared with
satellite and in situ system capabilities. It sets out the potential role for satellites, balloons, aircraft
reports, ships, etc. without pre-empting judgement on the best or most cost-effective mix of
observations.
The user requirements are user oriented, not system dependent; they are intended to be
technology free in that no consideration is given to the type of measurement characteristics,
observing platforms, or data processing systems that are necessary (or even possible) to meet
them. The requirements are aimed at the 2005-2015 time frame. The comparison of requirements
to capabilities utilizes the database summarizing both. As the database changes to better reflect
the user requirements as well as existing and planned observing capabilities, the RRR must be
performed periodically.
It is recognized that guidance provided by the WMO to satellite operators and other
agencies will be only one of many inputs affecting their decisions on future systems, which will be
required to meet national or regional objectives and will be constrained by available resources.
However, it is hoped that guidance at this level will be helpful in promoting an integrated global
observing system that provides the maximum benefit to WMO Members.
It is not intended that the process of reviewing requirements and providing guidance in
this way should replace the need for detailed activities on the design of instruments and systems,
but rather that general guidance should be provided on the users' requirements for these systems.
The detailed specification of instruments and systems will remain a task for relevant agencies, with
appropriate technical advice from specialists in the user community.
As shown with many examples throughout this technical document, the space segment of
the GOS provides global coverage, with its data being used in a variety of application areas. The
different capabilities of the two constellations are complementary and are necessary parts of the
space-based component of the GOS. Both constellations are capable of accomplishing data
collection and data dissemination missions. With respect to current WMO requirements, the polarorbiting satellites perform the following missions: imagery; data-collection; direct broadcast; and
atmospheric soundings; with today’s geostationary satellites being tasked to perform the same
missions with the exception of atmospheric sounding.
The ground segment of the space-based component of the GOS allows for the reception
of signals and DCP data from operational satellites and/or the processing, formatting and display of
environmental observation products that are distributed to local users, or over the GTS, as
required. This capability is normally accomplished through receiving and processing stations of
varying complexity, sophistication and cost.
In implementing satellite systems in response to WMO requirements, operational
environmental satellite operators make the satellite data reliably available to other Members and
inform them of the means of obtaining these data. “Reliably available” is a most important concept
and one that currently separates experimental satellites from operational environmental satellites.
Operational satellites, in a WMO context, are those satellites with a guaranteed replacement policy
that ensure continuity of service for WMO Members, either through in-orbit spares or an ondemand launch policy. Today’s operational environmental satellite operators expend great effort to
meet the accuracy, timeliness, and the temporal and spatial observational requirements of the
2-5
GOS. It is important to note here, that operators of some experimental satellites are providing
valuable information to WMO Members in the form of data for determining sea surface winds, sea
level topography and direct broadcast of very high resolution multi-spectral imagery. This
important paradigm shift is one of the major factors that will shape the future space based portion
of the GOS.
In order to meet the observational and service requirements for the space-based
component of the GOS, the number of satellites in polar orbit is sufficient to provide global
coverage at least four times per day for instruments with horizon-to-horizon scanning. Typically,
this requires one satellite in ante-meridian (a.m.) orbit and one in post-meridian (p.m.) orbit. The
number of satellites in geostationary orbit is sufficient to obtain observations, typically at 30 or 15
minute intervals, throughout the field of view between 600 S and 600 N. This implies the availability
of five satellites, near-equally spaced around the equator. Contingency plans, involving the use of
in-orbit spares and rapid launch call up of replacement satellites are either in place or under
development to maximize reliability and utilization.
The imagery and sounding capabilities are of the greatest accuracy possible,
independently or in conjunction with surface-based observations. The quantitative data and
qualitative information allow the determination of vertical profiles of temperature and humidity; land
and sea surface temperature; wind fields at the surface and aloft; cloud amount, type and cloud top
temperature and height; snow and ice cover; and radiation balance.
Direct broadcast and data-dissemination are provided in two data streams: a low and a
high data rate stream. They provide either direct or near-real-time data dissemination of cloud
imagery and, to the extent possible, other real-time data of interest to WMO Members. Satellite
operators also ensure the greatest possible compatibility between their different systems, and
publish details of the technical characteristics of their instrumentation, data processing and
transmissions, as well as the dissemination schedules. The frequencies, modulations, formats and
orbital de-phasing between the morning and afternoon satellites are arranged to allow a particular
user to acquire data from a particular satellite by a single antenna and signal processing hardware.
The means for data dissemination are continually evolving to take advantage of new technologies,
for example, the use of Internet and commercial telecommunication networks.
Data collection systems are capable of providing for the collection and relay of data from
various kinds of observing and Data Collection Platforms (DCP) in support of all WMO
programmes. To ensure compatibility, satellite operators have established and maintain the
necessary technical and operational coordination. A number of channels are identical on all
geostationary satellites to allow movement of mobile platforms between various geostationary
satellite footprints. Satellite operators publish details of the technical characteristics and
operational procedures of their data-collection systems, including the admission and certification
procedures.
The ground segment includes all the necessary facilities for operational control of the
satellites by the satellite operators as well as WMO Member user stations. All WMO Members
have endeavoured to install in their territory at least one user station for cloud imagery data from
the polar satellite constellation and at least one such station for receiving data from a geostationary
satellite.
References:
[1]
[2]
Fourth Report of the WMO on the Advancement of Atmospheric Sciences and Their
Application in the Light of Developments in Outer Space, WWW - Phase I, August 1965;
Fifth Long-term Plan.
CHAPTER 3
CHALLENGES FOR THE OBSERVING SYSTEMS
Meeting remote sensing requirements
These are exciting times in satellite remote sensing. Monitoring of the Earth’s
environment has become an international endeavour. No one country has the observational
systems necessary to provide the data it needs for its environmental monitoring and prediction
operations. In the past decade, and more so in the next decade, satellite remote sensing
contributions to the Global Observing System are being made by an increasing number of
international partners. The collaboration and coordination among the international satellite
community continues to increase.
The demands for environmental data are enormous, ranging across all components of the
Earth system - atmosphere, oceans, and land. The data requirements cover a broad range of
spatial and time scales - 100s of metres and minutes to global, seasonal and inter-annual to
decadal and centennial time frames. Alongside the operational satellites, the research satellites
are providing advanced observations of the Earth and its atmosphere and in the future the
distinction between research and operational platforms will narrow.
It is important to note that satellites are one component of an integrated Global Observing
System. The surface-based component of the GOS is comprised of a variety of instruments on
balloons, planes, ships, buoys, and land surfaces. It remains a challenge to identify the best mix of
the space-based and surface-based components that will meet environmental monitoring and
prediction requirements.
To achieve the above demands will require significant advances in scientific
understanding, observing and computational systems, scientific research and modelling, and
extended infrastructure to deliver enhanced and new services effectively and efficiently to end
users. We are now entering an era rich with new scientific insight and technological innovations
that allow us to observe, understand and model the functioning of the entire Earth system.
Figure 3.1 - Hurricanes Danielle and Earl
shown on the same colour enhanced GOES8 image as Tropical Storm Isis, on 2
September 1998. (NOAA/NCEP)
Figure 3.2 - Tropical Cyclone Paula passing
over Vanuatu in late February 2001, shown in
a GMS-5 satellite image with superimposed
surface winds from QuickScat. (MSNZL)
3-2
Figure 3.3 - GMS-5 image of a Typhoon
close to Japan with superimposed cloud-track
winds. (Blue vectors are low-level clouds,
pink vectors are high-level clouds and yellow
vectors are winds derived by tracking water
vapour). (JMA)
Figure 3.4 - Tropical Cyclone between
Madagascar and Mozambique February
2000. (PUMA)
Improving Understanding of the Earth System, its Components and their Interactions
Comprehensive Models
The challenge of Earth system modelling is to develop unified comprehensive models of
the significant linkages and interactions among the component parts of the system. Such models
can be used to test the sensitivity of the Earth’s variable environment to various processes or
forcings. They can also be replaced by simplified representations (e.g., climatological or similarly
specified values) to reduce the computational load. Coupled Earth system models are needed to
assess the Earth system variability and change, and the impact on agricultural, economic, and
industrial activities of any such changes. Models are also required to provide routine and reliable
seasonal-to-interannual and longer-term climate prediction, and will form the basis for data
assimilation systems that encompass the entire Earth system.
Figure 3.5 – Schematic of the Earth system
3-3
Advances in computer technology will allow the compilation and running of models that
embrace a wide enough range of spatial and temporal scales to explicitly simulate the dynamical
connections between different natural categories of phenomena, for instance in the case of the
atmospheric circulation by:
-
Linking global-scale changes in the atmospheric general circulation to organized
weather systems such as tropical cyclones, frontal systems or squall lines;
-
Linking organized weather systems to convective-scale phenomena, tornado- and
rain-generating storms, and a diversity of terrain and terrestrial ecosystems (e. g.,
cloud ensemble models; limited-area atmospheric-hydrologic models, etc.);
-
Linking cloud-scale dynamics to microphysical and chemical processes.
Data Assimilation
Four-dimensional data assimilation will play an increasingly important role both in
facilitating advances and in reaping their ultimate scientific benefit. Data assimilation provides a
rigorous framework for integrating information from the models and from the highly heterogeneous
observation systems. It is a powerful tool not only for adding information to model-simulated fields
through the use of observations, but also for detecting and diagnosing weaknesses in both the
models and in the observations. This ability to help detect systematic model errors gives data
assimilation a direct role to play in the area of model development. In data assimilation mode, a
coupled Earth system model is continually confronted with the actual observational data and is
"corrected" by them as the model integration proceeds. Thus four-dimensional data assimilation
systems will be used to validate increasingly sophisticated and complete representations of
different physical, chemical and biological processes, as well as to measure the linkages among
these processes and dynamical processes at different scales.
Linkage between observable processes
Another very significant direction of progress will be the introduction of progressively more
realistic representations of linkages between different types of processes, e. g., climate-transport
dynamics-chemistry interactions, climate-biosphere-hydrology interactions, ice-sheet and sea-ice
dynamics and forcing by atmospheric and oceanic circulations, climatic impacts on the spreading
of disease vectors, etc. The emphasis of model development efforts will shift from representing
large-scale dynamical transport and plane-parallel approximations of radiative and other fluxes,
towards the detailed physical, chemical and biological processes that are actually taking place in
the system. More attention will need to be given to the integration of modelling efforts and
observational field studies, each supporting the other for optimal scientific advances.
Archiving
There is no possibility of advancing scientific research or improving operational data
assimilation systems without the availability of retrospective observed data and products. The
long-term stewardship of this information is only possible through planned, carefully constructed
systems of data acquisition, processing and archival. Such systems would be rendered worthless
without the final piece of the puzzle: a system to allow user access to the data archives. There are
many challenges concerning the future processing of satellite observations for the purpose of
quality assurance, long-term archiving, dissemination to users and overall stewardship. Perhaps
the largest involves the sheer volume of the data, which is becoming available today and will
continue in abundance from the proposed high-resolution sensors during the next decade.
3-4
Communications
New communications technologies are necessary to move the vast amounts of
information from these observing and modelling capabilities among widely diverse users and
suppliers. On-board data processing and intelligent communication among satellites in orbit, or
between space and the Earth’s surface, allow managing large data and information volumes and
make a future system more efficient. The ability to mine data archives and fuse data types will also
make communication more efficient, enabling the ordering and delivery of tailored information
products. The ability to go back and access, search and process historical data records for
scientific research and new products and services is essential for Earth system science. As with
expanded applications of GPS today, the future will see a broad array of environmental information
available to users at their desktops, or even in remote locations via personal information
assistants.
Common among any such scenario is the requirement for a flexible and reconfigurable
observing architecture that can:
-
Accommodate new capabilities without affecting existing ones;
Receive contributions from all interested nations;
Integrate observations from a variety of space-based, sub-orbital, and ground-based
platforms;
Tailor observations and information as required for a variety of applications;
Establish common protocols and standards for sharing readily observations and
information among the components of the observing networks and with diverse users.
Research to Operations
Development of capabilities will remain the responsibility of national research and
technology agencies, but collaboration between research and operational systems, and among
nations, will be essential for the true utility of these capabilities to be realised. A key challenge will
be the transition of new technologies into operational observing systems. Collaboration among
research and operational agencies throughout the concept definition, design, development, and
implementation of such systems is essential. These collaborations may take the form of ‘transition
missions’ that serve both active research and operational demonstration goals, as with the
US/European partnership in ocean altimetry. Such steps accelerate the use of new observing data
in operational forecasting ahead of the availability of a first truly operational satellite system. The
observations of both operational and research satellites will both be used in the analysis. Thus the
distinction between them, within the GOS will be seamless. The role of the WMO in bringing
research and operational agencies of nations around the world together will be vital in making this
vision of the future a reality.
CHAPTER 4
NEAR-TERM CONFIGURATION OF THE SPACE-BASED COMPONENT OF THE GLOBAL
OBSERVING SYSTEM (GOS)
Geostationary satellites provide a continuous view of weather systems making them
invaluable in following the motion, development, and decay of atmospheric phenomena. Even such
short-term events such as severe thunderstorms, with a life-time of only a few hours, can be
successfully recognized in their early stages and appropriate warnings of the time and area of their
maximum impact can be expeditiously provided to the general public. The warning capability has
been the primary justification for the geostationary spacecraft. The polar-orbiting satellite system
provides the data needed to compensate the deficiencies in conventional observing networks; it is
able to acquire data from all parts of the globe in the course of a series of successive revolutions.
The polar-orbiting satellites are principally used to obtain daily global cloud cover and quantitative
measurements of surface temperature and temperature and water vapour soundings in the
atmosphere; these global data are acquired by a single set of observing sensors. Together, the
polar-orbiting and geostationary satellites constitute a truly global meteorological satellite network.
Currently, observations from both polar orbiting and geostationary instruments are close
to achieving accuracies, resolutions, and cycle times for key meteorological parameters such as
cloud type, temperature and humidity profiles, (from infrared and microwave sensors), and upper
level winds (from tracking the movement of cloud and water vapour features). The polar and
geostationary platforms for the present and the next ten years are summarized in the following
paragraphs; the instruments on board and their capabilities can be found in Annex IV.
Figure 4.1 - Meteosat visible image showing an intense depression off the west coasts of Europe
together with its associated trailing cold front. A mature depression is shown off the coast of
Norway with its occluded front over the Baltic Sea. Widespread showery activity in the cold air over
the Atlantic is contrasted with the largely cloud-free conditions over much of France and the Iberian
peninsula. (EUMETSAT)
4-2
Figure 4.2 - Cloud clusters indicative of heavy local rainfall and a squall line, seen on a Meteosat
infrared image enhanced through colour coding of the cloud-top temperature. (PUMA)
Current and Future Polar platforms
Polar orbiting satellites provide global coverage twice a day. Their orbital altitude of 850
km makes it technically feasible to make high spatial resolution measurements of the
atmosphere/surface. To provide a reasonable temporal sampling for many applications at least
two satellites are required, thereby providing 6-hourly coverage. However, observational user
requirements already specify more demanding temporal coverage equivalent to at least 3-hourly
coverage. A backup capability exists by reactivating 'retired' platforms and this has been
demonstrated recently. Since 1979, coverage with two polar orbiting satellites has been achieved
most of the time. They carry a multi-spectral imager (usually with 1 km resolution visible, near-IR,
and IR window bands for observing cloud cover and weather systems, deriving sea surface
temperature, detecting urban heat islands and fires, and estimating vegetation indices), a multispectral infrared sounder (usually with roughly twenty broad spectral bands of 20 km resolution for
deriving global temperature and moisture soundings), and a multi-spectral microwave sounder
(most recently with twenty microwave channels of 50 km resolution for deriving temperature
soundings even in non-precipitating cloud covered regions).
Current operational polar orbiters include the NOAA series from the USA and the
METEOR, RESURS, and OKEAN series from the Russian Federation and the FY-1 series from
China. They provide image data that can be received locally. The NOAA satellites also enable
generation of atmospheric sounding products that are disseminated to NWP centres on the GTS.
In the future, the NOAA AM satellite will be replaced by the METOP satellites provided by
EUMETSAT and the NOAA PM satellite will transition to the NPOESS series. The Russian
Federation METEOR series will evolve into the METEOR 3M series and the Chinese FY-1 series
will be replaced by the FY-3 series.
Research and Development missions continue to make many contributions in the area of
polar orbiting remote sensing measurements. To maximize the impact of those data and the
associated expenditures in resources (manpower and financial) by operational users, space
agencies are committing to (a) open and timely access to the data in standard formats, (b)
preparation of the community for new data usage, and (c) data continuity.
4-3
NASA’s Earth Observing System includes multiple platforms. Terra has been in an AM
orbit since December 1999 and is providing global data on the state of the atmosphere, land, and
oceans, as well as their interactions with solar and Earth radiation. Aqua followed in a PM orbit in
May 2002 and will provide climate related data with respect to clouds, precipitation, atmospheric
temperature / moisture content, terrestrial snow, sea ice, and SST.
Figure 4.3 – SeaWiF’s map
of phytoplankton pigment
concentration
over
the
oceans together with a
vegetation index over the
land areas, showing global
biomass in one image. Ocean
colours range from dark blue
(less than 0.1 mg/m3 up to
red in some coastal areas (10
mg/m3). (GSFC)
The NASA/CNES Topex/Poseidon and Jason-1 satellites provide a wealth of observations
on the status of the ocean topography, and this is used in both short-term storm analysis and
climate studies. In 2004 Aura will provide a suite of chemistry measurements focusing on
atmospheric trace gases in the upper troposphere and lower stratosphere. In addition, the Earth
Observer series has started providing hyperspectral vis/NIR data.
Figure 4.4 - Global sea levels, showing the region (red
box) used to determine the sea level index
(Topex/Poseidon)
Figure 4.5 - Map of Sea Level Anomaly in the
Eastern Mediterranean from Topex/Poseidon
Data. The scale ranges from +/- 30 cm (red to
blue). (CLS)
ESA launched the ENVISAT platform in March 2002. It will provide measurements of the
atmosphere, ocean, land, and ice over a five year period. ENVISAT has an innovative payload
that will ensure the continuity of the data measurements of the ESA Earth Remote Sensing (ERS)
satellites, as well as facilitating the development of operational applications. Thereafter, several
Earth Explorer missions are planned to study the gravity field (2005), atmospheric dynamics
(2007), polar ice (2004), and soil moisture and salinity (2006).
4-4
Figure 4.6 - Distribution of stratospheric ozone
over the northern hemisphere on 30 November
1999 from GOME/ERS-2 data (DLR)
Figure 4.7 - Distribution of stratospheric NO2 over
Europe, derived from GOME data on ERS-2.
(DLR)
Figure 4.8 - Distribution of SO2 of anthropogenic origin over Europe, obtained from ERS-2/GOME
data. (AWI)
NASA and NASDA launched in 1997 a joint mission, the Tropical Rainfall Measuring
Mission (TRMM). TRMM is designed to monitor and study tropical rainfall and the associated
release of energy that helps to power the global atmospheric circulation shaping both weather and
climate around the globe. It also provides information on soil moisture. By combining TRMM
precipitation data with ocean vector winds data from QuikSCAT (launched in 1999), researchers
have demonstrated the ability to significantly improve hurricane track and landfall prediction.
4-5
Figure 4.9 - Global soil-wetness index computed from data from the Precipitation Radar on TRMM
(NASDA)
NASDA will be continuing their ADvanced Earth Observing Satellite mission with ADEOS2 in 2002. The ADEOS-2 mission is designed to monitor and investigate the causes of frequent
climate changes occurring in the world, expansion of the ozone holes, and global environmental
changes. NASDA will follow this with the Advanced Land Observing Satellite (ALOS) in 2003 that
will utilize advanced land observing technology. Later this decade, the Global Change Observation
Mission (GCOM) will be aimed at observing parameters over the long term (as long as 15 years),
and to understand the mechanism of the global environmental change. GCOM-A1 will observe
ozone and greenhouse gases and GCOM-B1 will monitor energy and the general circulation from a
sun-synchronous orbit.
Canada launched RADARSAT-1 in 1995; it is equipped with synthetic aperture radar
(SAR) and is proving to be useful in a variety of fields, including disaster and environmental
monitoring.
Indian Remote Sensing Satellites (IRS) IRS-1C and IRS-1D were launched in 1995 and
1997, by the Indian Space Research Organization (ISRO). The IRS satellites are equipped with an
optical sensor – Panchromatic sensor (PAN) that has the highest level of resolution, at 5.8m, of
the current Earth observation satellites. It is being utilized for land application studies.
The People’s Republic of China is providing the newest series of polar-orbiting satellites,
the FY-1 series. The FY-1 series has greatly enhanced imaging capabilities from polar orbit with
its 10 channel radiometer that includes the same five channels as found on NOAA’s AVHRR and
five new channels.
4-6
Figure 4.10 - Duststorm (red-orange tones) over China observed by the PRC’s FY-1C polar
satellite using an instrument with similar characteristics to AVHRR. (NSMC)
Current and Future Geostationary Platforms
The geosynchronous orbit is over 40 times higher than a polar orbit, which makes
measurements technically more difficult from geostationary platforms. The advantage of the
geostationary orbit is that it allows frequent measurements over the same region necessary for
now-casting applications and synoptic meteorology. A disadvantage is that a fixed full disk view of
the Earth is viewed from one satellite. Thus, at least five equally spaced satellites around the
equator are needed to provide global coverage; polar regions are either very poorly observed, or
not observed at all because of the large zenith viewing angles.
Currently, there is global coverage from geostationary orbit (more than 5 operational
satellites for image data and products (e.g., cloud motion winds) and 2 satellites are also providing
a sounding capability. Reactivating ‘retired’ platforms provides backup and there have been
several examples of this type of activity. The geo-imagers typically have 1 km resolution visible
and 5 km IR window bands for observing cloud cover and weather systems in motion and
estimating atmospheric motion vectors. The geo-sounders to date have eighteen broad spectral
bands of 10 km resolution for deriving atmospheric temperature and moisture trends in time.
Some of the satellites provide a real-time transmission capability to allow immediate
access to the imagery for real-time applications. Products are disseminated on the WWW’s Global
Telecommunications System (GTS) by the satellite operators for near real-time applications.
4-7
Figure 4.11 - Meteosat
infrared image colour coded
with
cloud-top
temperatures, together with
two hour forecast of cloud
edge locations (solid lines),
forecast
tracks
of
precipitation events (dashed
vectors) and areas of
current cloud development
or dissolution (black lines).
(ZAMG)
At present there are satellites at 0 longitude and 63E (Meteosat 5 and 7 operated
EUMETSAT), 76E (GOMS operated by the Russian Federation), 105E (FY2B operated by the
People's Republic of China), 140E (GMS-5 operated by Japan), and 135W and 75W (GOES 8
and 10 operated by the United States of America (USA).
All geostationary satellite instruments are evolving to more spectral coverage and faster
imaging. In 2003, Europe is moving to the SEVIRI. Japan will launch JAMI in 2003. China will
launch another in the FY 2 series of imagers in 2004. India is enhancing their geo capabilities with
INSAT-3A and Metsat, to be launched by 2002, and INSAT-3D (to be launched in 2004). USA will
evolve to an Advanced Baseline Imager and Sounder in 2012.
Enhanced monitoring of selected Earth system components
New observing capabilities demonstrated in a research mode during the next decade will
become part of an operational observing system of the future. These include:
Atmospheric Sounding
Continuous observation of tropospheric temperature/moisture profiles, wind pattern and
moisture inflow in the far field around weather systems, where the cloud cover is broken, will be
demonstrated in 2006. The Geosynchronous Imaging Fourier Transform Spectrometer (GIFTS)
instrument is being developed to demonstrate a unique ability to peer continuously through many
layers of the atmosphere from geosynchronous orbit with the precision and accuracy of
atmospheric sounding capabilities being developed for low Earth orbit systems (i.e., 1K accuracy
and 1 km vertical resolution). The test instrument is intended to map the three-dimensional
distribution of water vapour, determine atmospheric temperature profiles and detect the presence
of trace gases, such as carbon monoxide, ozone, and methane) at different altitudes in the
atmosphere
Atmospheric Winds
Global wind field measurements will be directly applicable to numerical weather
prediction, and extremely valuable for scientific diagnosis of large-scale atmospheric transport,
weather systems, and boundary layer dynamics. A space-based Doppler lidar system is being
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developed to deliver relatively sparse wind data; progress in space-borne laser technology will
continue in order to make this active sounding technique available to operational uses.
Global Precipitation
Quantitative measurement of the time and space distribution of global precipitation is the
next highest climate research priority beyond atmospheric temperature and moisture, and an
essential requirement to understand the coupling among atmospheric climate, terrestrial
ecosystems and water resources. Satellite remote sensing is the only means to acquire global
rainfall data, considering the paucity of surface observations over the ocean and sparsely
populated land areas. Measurement of global precipitation would likely be based on frequent
observations from a constellation of passive microwave sensors. Meanwhile, detailed vertical
atmospheric distribution of rain data would be provided by a common rain radar satellite for
refinement and validation of retrieval algorithms for all satellites in the constellation. An early
demonstration of this concept is being conducted using the TRMM, Aqua and ADEOS II research
satellites in tandem with operational meteorological satellites. These extend to the TRMM-like
precipitation measurements to extra-tropical parts of the world for the first time, and demonstrates
the concept of 3-hour global precipitation products with utility to a broad range of WMO Members’
objectives.
Figure 4.12 – Global Precipitation Measurement (GPM) constellation
Soil Water Content
At present, near-surface soil water content is the only primary hydrologic variable that is
not measured at large spatial scale. Scientific evidence shows that near-surface soil water content
is the most significant indicator of the state of the terrestrial hydrologic system, and is the
governing parameter for partitioning rainwater among evaporation, infiltration, and runoff. Large
antennae will be needed to meet these requirements at low microwave frequencies; these remain
a significant technological challenge.
Ocean Surface Salinity
Ocean salinity, even more than temperature, controls the dynamics of the deep ocean
circulation and long-term climate. Sea surface salinity (SSS) determines the depth to which cold
surface water may sink to form intermediate and deep ocean water masses. Despite the scientific
significance of SSS, there is almost a total lack of systematic ocean salinity measurements worldwide, except for occasional oceanographic cruises and automated measurements on some
merchant vessels. Developing low-frequency microwave radiometry techniques for remote
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sensing of sea-surface salinity is a recognized objective of technology development by several
space agencies.
Cold Climate Processes
Passive and active (radar) microwave remote sensing methods are being considered by
Europe, the US, and Japan to determine the most effective means to acquire information globally
on snow extent and water equivalent, soil freezing and thawing that strongly affect the hydrologic
regime of river basins at high latitudes.
Figure 4.13 - Snow cover extent for North America from the Moderate Resolution Imaging
Spectroadiometer (MODIS) on Terra. The red and yellow lines represent the average March and
February snow lines, indicating below-average snow cover in 2001.
Carbon Sources and Sinks
Available in situ measurements of near-surface atmospheric carbon dioxide
concentrations have been used to constrain inverse models of atmospheric transport and predict
hemispherical scale distributions of carbon sources and sinks. The ability to make direct spacebased measurements of atmospheric carbon dioxide concentrations, with sufficient accuracy and
precision, will provide improved global coverage and overcome problems associated with local
effects that complicate surface measurements in the interior of continents. Several research
groups are studying a number of passive (spectrometric) or active (lidar) techniques to measure
total column and vertical profile abundance of carbon dioxide with an Earth orbiting satellite.
Vertical profiles of clouds and aerosols
Atmospheric aerosol content is subject to substantial variation in amount and type, as
concentrations are driven by natural and human activities, including agricultural and industrial
practices. In the first half of this decade, the first attempt at global observation of the three-
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dimensional structure of clouds and aerosol distributions will be undertaken. These involve active
remote sensing systems (i.e., lasers/lidars and microwave radars) rather than the passive remote
sensing systems such as radiometers that are common today. Due to the long term nature of
climate change research, such systems are likely candidates to become part of the operational
climate observing system in the future.
Figure 4.14 - The first global, four-dimensional profiles of clouds and aerosols in the atmosphere will
be provided by the Cloudsat and Calipso satellites. These will fly in formation with other atmosphere
observing satellites to facilitate the combining of data into integrated atmospheric research products.
Data handling and communications
The scientific understanding and applications benefits enabled by such technologies will
rely on efficient data handling and communications. This will include onboard information
processing and large bandwidth space-to-space and space-to-ground communications
On-board information processing
On-board processing will be crucial in order to reduce to manageable proportions the
volume of data which must be transmitted to the ground, to provide useable data products directly
to user sites, and/or enable automated spacecraft operation modes minimizing the need for
expensive operation control from the ground. A variety of technology breakthroughs are needed,
including: adaptive computing “toolkits” for rapid prototyping of new field programmable gate
arrays (FPGAs); on-board reconfigurable data path processors for science data processing;
advanced holographic memory development; ultra high density fast readout optical storage
devices; optimized compression techniques; and on-board data mining tools.
Large bandwidth space-to-space and space-to-ground communications.
Constellations of the future may also require substantial spacecraft-to-spacecraft
communications, either for coordinating observing tasks among satellites or for transferring
observations and information directly from satellites onto the desktop computers of users. To
address these challenges, very high speed optical communications transceivers provide the best
prospect for high data-rate links compatible with large data volumes, relatively low power use, and
available (yet-unallocated) bandwidth. However, optical data links are sensitive to the presence of
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clouds, so that new mitigation strategies that increase link availability will be needed, based on the
use of relay satellites and ground-station diversity. Low Earth and geostationary orbit cross-links is
the key system level concept for reliable data return from space. Incorporating data delivery to
two-three ground stations, strategically located so as to exhibit anti-correlated cloud cover,
increases ground station availability.
CHAPTER 5
VISION FOR THE FUTURE: A 2020 PERSPECTIVE
The extension of reliable weather forecasting and assessments of the causes and course
of longer term Earth system change are two major accomplishments of WMO and its partner
organizations that have a demonstrable value to humanity. But they also open a door towards a
greater range of possibilities in the future. Annual losses due to natural disasters, most of which
are weather-related, exceed on average 50,000 lives and tens of billions of dollars. Some
research activities indicate longer-term climate change will impact the distribution, frequency, and
intensity of severe weather events. Annual decisions on food and fibre production, multi-year
investments in infrastructure development, and management of fresh water resources to name just
a few contemporary socio-economic issues, would see a tremendous benefit from reliable,
extended services and products, such as:

30 minute warning of very destructive weather events; for example, tornado
prediction beyond 10 minutes is notoriously difficult but necessary in susceptible
areas;

1-12 hour severe weather events; greatly increased skill for shorter-term severe
weather events covered by Nowcasting and Very Short Range forecasting is possible;

5 day hurricane track prediction to +/-30 km; to reduce the number of false warnings
resulting from the present landfall location uncertainty of 400km at 3 days;

10-14 day weather forecast; new measurements, especially tropospheric winds, and
substantial advances in modelling capability can push short term weather prediction
out to the limits;

12 month regional rain rate including Monsoon related forecasts; recent efforts in
global water cycle modelling indicate the potential to deconvolve global-scale water
cycle variation into regionally specific projections;

15-20 month El Niño prediction; “hindcasting” of the two most recent El Niño events
indicates that this is possible with an adequate system of space-based and in situ
observing capability paired with focused modelling efforts;

10 year climate forecasts; decadal-scale climate prediction is theoretically possible
with the extension of the research systems now being deployed to the operational
systems of tomorrow.
Meteorological satellites provide essential data for weather forecasting to national weather
services around the globe. This has in large part been due to direct broadcast and through global
sharing of data and science. Satellites offer high-resolution digital renderings from a range of
spectral bands whereby both qualitative and quantitative information about the atmosphere,
clouds, the land and the sea surface properties are deduced. The need for a global view requires
us to embrace the concept of a composite observing system where the distinctions between
geostationary, polar and low Earth orbiting satellites, research and operational satellites, and
various sensors are minimized. To prepare for the daunting task of monitoring and understanding
the Earth’s system from these new data we must work in a global science collaboration and
through operational partnerships.
Mankind’s ability to observe the Earth and its environment from space has always been in
transition. Indeed, the operational satellite system in this first decade of the new century is
undergoing a metamorphosis, and it will undergo a marked alteration in appearance, character and
function.
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As the space based remote sensing system of the future develops and evolves, four
critical areas (all dealing with resolution) will need to be addressed in order to achieve the desired
growth in knowledge and advanced applications. They are:

spatial resolution – what picture element size is required to identify the feature of
interest and to capture its spatial variability;

spectral coverage and resolution – what part of the continuous electromagnetic
spectrum at each spatial element should be measured, and with what spectral
resolution, to analyze an atmospheric or surface parameter;

temporal resolution – how often does the feature of interest need to be observed; and

radiometric resolution – what signal to noise is required and how accurate does an
observation need to be.
Each of these resolution areas should be addressed in the context of the evolving space
based observing system wherein the satellite(s) exist, or will exist - this will call for new approaches
in all aspects of the operational satellite arena.
Satellite System Characteristics
Responding to the requirements above, the future satellite systems will be a hybrid of
different orbits needed to achieve the desired result. In turn, the satellite systems must be
continuously “tuned” with the master system, so that a fully global environmental observing system
emerges.
The long-term continuity of well-calibrated and characterized observations from satellites
is an essential element in the construction of a climate record, and marks a significant shift from
the use of the data sets in the shorter term meteorological forecasting. Having an understanding of
the quality of the data and the long-term sensor/system performance becomes a very important
issue for climate applications. These factors have significant implications in terms of the quality
control of the data sets and will impact both the observational regime and the subsequent data
handling and archiving. The need to understand the basic long-term sensor performance for
individual sensors and between systems will require a focus of effort on the analysis of the longterm sensor performance and inter calibration. This will enable the data to be used sensibly in
product generation, data assimilation and climate modelling.
Creating continuity of the core observations already being made in 2002 will be one
priority. This is a complex issue because developments in sensor capability – both spectrally and
spatially - will result in the need for formal inter-calibration between systems as new systems are
introduced. Furthermore, the long-term sensor performance for a specific system will need to be
carefully monitored. Studies with Meteosat archive data have already shown that variations, that at
first analysis are significant, are in fact due to variations in the sensor performance. It will,
therefore, be necessary to ensure the ancillary data are stored alongside the satellite data
observations in an accessible and usable format.
For future systems there will also be a need for better management and knowledge of the
orbit for LEO satellites. Orbit drift, as measured by the nominal equatorial crossing time for sun
synchronous satellites, has, for certain sensors, a significant impact on the sensor response.
Controlling the orbit, or at the very least recording the actual orbit will become a routine
requirement for Earth observation satellites. This is already an acknowledged need in certain
areas, such as precise ocean surface topography from Topex/Poseidon and Jason-1. The use of
GPS class satellites and fixed Earth stations will become routine in this respect.
Data coverage will remain a major issue for LEO satellites. There will remain a drive for
higher spatial and temporal resolution, which can only be satisfied by having several satellites
making the same or similar observations. However, this in itself will require better harmonization of
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orbits between agencies in order to optimize the coverage. This will also provide a framework for
research satellites to be used in parallel with long-term operational systems. In this way the
operational community will be able to take advantage of research data sets, alongside the original
conceived research programmes. It will also provide a good mechanism for the introduction of new
sensors in a controlled environment that will maximize the cross calibration to support the longterm data record.
Geostationary Satellites
The geostationary satellite will play a particularly important role in the metamorphosis of
the space-based component of the GOS, and in helping frame the context of the system’s
development and evolution.
Amongst the more important capabilities that will be developed and brought to fruition in a
geostationary context are: high spatial resolution hyperspectral imaging and atmospheric sounding
capability; routine rapid interval full disk imaging; routine full disk sounding at a variety of temporal
resolutions; and, exploration of advanced sensor technology for lightning detection and microwave
applications. To maximize the information available from the geostationary and low Earth orbiting
satellite system, geostationary satellites will be placed “nominally” at a 60-degree sub-point
separation across the equatorial belt. These important capabilities are addressed in more detail
below.
High spatial resolution hyperspectral imaging and atmospheric sounding capability
In 2002, imaging from geostationary orbits has evolved from simple low spatial resolution
broadband visible imagers to digital high spatial resolution multi-channel imagers. Products
derived from those digital data sets are providing valuable information for a variety of applications
ranging from nowcasting to global NWP. In the atmospheric sounding arena multi-channel
instruments are being utilized to characterize atmospheric stability features important in the
development of severe convective storms.
Issues dealing with clouds and solar zenith angle make the geostationary orbit important
for many applications as well as their current meteorological use. They include areas such as
smoke and atmospheric optical property monitoring (including water vapour), as well as land,
vegetation and selected ocean colour applications. This points towards the development, by 2025,
of hyperspectral imaging capability from geostationary altitude in the range between 0.4 and 2.5
microns. For certain of those applications, the spatial resolution available from geostationary orbit
will not be adequate to meet the user needs. However, when viewed from a system’s context, the
information from the geostationary observations will be able to place the lower Earth orbiting
observation into a perspective that deals with temporal evolution and variability (such as algae
blooms) that cannot be captured by the higher resolution low Earth orbiting sensor.
Furthermore, science and applications that develop during the next twenty years will allow
us to merge the hyperspectral data streams into channels, pseudo-channels and spectral
combinations that will allow for the development of new applications from a uniformly calibrated
spectral suite of observations. The merging of sensor spectral characteristics for use in polar and
geostationary orbit is a critical and necessary step forward that will enable the “satellite system’s”
full exploitation for applications and science studies that range from nowcasting to climate and
across applications areas that range from ecology to oceanography and into weather.
Routine rapid interval full disk imaging
In 2002, 15 minute interval images are becoming routine for the USA and Europe, with
operations that provide more frequent views at times of severe weather (but at the expense of
larger area views). Plans for future USA GOES and European METEOSAT satellites call for more
rapid interval imaging, as do plans for China’s and Japan’s geostationary satellites. The
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advantages of frequent interval imaging are ubiquitous, and range from better observations of
cloud pattern evolution for nowcasting to more accurate cloud motion vectors to having more
opportunities for cloud free views of a scene for derivation of products such as sea surface
temperature.
The expectation by 2025 is that imaging frequency will be 5 minutes at the full disc, but
with the option for slower scan rates to increase signal to noise for some important hyperspectral
applications.
Routine full disk sounding at a variety of temporal resolutions
By 2025, all geostationary satellites should aim to support a high spectral resolution
atmospheric sounding capability at a one hour interval, but with the option for slower scan rates for
some applications such as trace gas monitoring. This is based on the perceived needs of
nowcasting applications, tracking of atmospheric water vapour at different levels, and possibly
selected atmospheric gases. This takes into account the capabilities expected by the low Earthorbiting component of the system
Exploration of advanced sensor technology for lightning detection and microwave
applications
By 2025, the geostationary satellites will carry instrumentation that will allow for the
detection of lightning from space.. It is also expected that microwave technology will be tested
from geostationary satellites by the mid-2020s. The main utilization will focus on rainfall
estimation, and science developed from such an endeavour will be evaluated in the context of a
low Earth orbiting global precipitation mission to determine the role of geostationary microwave
sensing as part of the space-based component of the GOS.
60 degree sub-point separation for geostationary satellites
The aim for 2025 is for geostationary satellites to be placed “nominally” at a 60-degree
sub-point separation across the equatorial belt. This separation provides for good viewing to about
70 degrees N/S, as well as an excellent opportunity for stereographic cloud height assignment,
which with a 5-minute full disk allows for very good asynchronous stereo. Furthermore, six
satellites in equatorial orbit (all with similar imaging and sounding capabilities) provide for a more
substantial backup capability. Thus, the failure of a satellite could result in reallocation of the
geostationary constellation to 72-degree separation, similar to that in 2002, while awaiting
replacement to a fully robust system.
Low Earth Orbit Satellites
Polar and low inclination orbiting satellites
Current, polar orbiting satellites provide global imaging and sounding coverage twice a
day, and remain the only source of higher temporal coverage in the Arctic and Antarctic regions.
The two current operational satellite configuration achieves measurements of the
atmosphere/surface with high spatial resolution and reasonable temporal sampling. The benefit of
hyperspectral visible and near infrared measurements for land usage, ocean colour, and aerosol
plus cloud property studies are already being demonstrated from experimental programmes using
VIS/IR and microwave frequencies. As the Global Observing System evolves, a balance or
compatibility in improvements in the four areas of spectral, spatial, temporal, and radiometric
resolution will be essential. User requirements in several applications areas (including numerical
weather prediction) indicate the need by 2025 for a four-polar-satellite system. This would provide
two sets of observations in both the morning and the afternoon, which with appropriate equator
crossing times would provide for adequate temporal sampling and contingency planning.
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Microwave, altimetry, scatterometry, radio occultation, and lidar systems will remain
unique to the low Earth orbiting satellites. In the 2025 period sounding will be accomplished with
combined radiometric (infrared and microwave) and geometric (radio-occultation) systems.
Passive and active remote sensing will be combined to offer the best measurement of water
vapour at resolutions commensurate with its natural variability. Altimetry will be pursued with a
two-orbit fully operational system with real time capability wide swath (non-scanning) altimeters to
enhance mesoscale understanding. Atmospheric wind profiles will be accomplished with Doppler
lidar systems, ocean surface wind vectors now employing both the current active techniques and
new passive capabilities. Sea surface Temperature (SST) will evolve from data combined from
LEO and GEO systems of measurements, whilst expanded ocean colour capabilities will include
increased horizontal resolution for coastal areas. SAR data from a multi-satellite system will
provide “wave mode” and sea ice/wave monitoring service and three dimensional surface
topographic information based on interferometric remote sensing techniques.
Major challenges for the next 25 years include





Coordination of orbits for LEO missions to generate maximum temporal coverage with
some necessary orbit redundancy;
Resolution of communications issues for real time availability of global data and
products;
Cooperation for calibration / validation / harmonization of basic products; and
Continuation of data collection services that are crucial for collection of in situ data;
Coordination of missions from different agencies to maximise the range of
observations.
Expansion of the polar satellite component of the GOS will be an international
collaboration. There will be efforts to facilitate contributions of multi- instruments to larger
platforms and formation flying of these platforms with single instrument small satellites to maximize
accommodation of scientific and operational requirements.
Coordination of international
contributions to the polar orbiting observing system to achieve optimal spacing for a balance of
spectral, spatial and radiometric and temporal coverage will be a major goal. Operational
continuation of research capabilities with proven utility to the GOS will need to transition to
operations without interruption of the data flow.
There will need to be a commitment for adequate resources to sustain research
developments necessary for improved utilization of these measurements, with parallel funding of
exploitation development by the relevant agencies. International algorithm development will assure
best talent participation and enhance uniformity in derived products and timely access to these
capabilities by all interested nations around the globe without restriction.
Challenges for the Future
Satellite sentinels at the Lagrangian (neutral gravity) L1 and L2 points would enable
around-the-clock monitoring of the entire globe, including the polar regions that are not wellcaptured from geostationary orbit. A satellite at L1, (on the Earth-Sun line between them) for
example, would see diurnal changes in ozone and atmospheric chemistry, which are little
understood today. A satellite at L2 (on the Earth-Sun line beyond the Earth) would be an ideal
global thermometer, since small global temperature changes would show up most clearly in
increasing night-time minimum temperatures.
New observations that are only imaginable today can also be foreseen. These include
photon-less remote sensing techniques that could, for example, probe the structure and
composition of the Earth below the surface, and new remote sensing devices based on quantum
physics principles.
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Operational weather and climate forecasting would benefit most from a smart constellation
of diverse satellites in various orbits capable of working together with each other and ground-base
observations. As an example, when a an emerging event such as a tropical storm, polar low or a
volcanic eruption is noted by a ground-based instrument, GEO or sentinel satellite at L1, it could
focus the attention of a web of sensors in low Earth orbit. The web would bring to bear active and
passive observing technologies across a broad span of the electromagnetic spectrum in order to
monitor the event’s behaviour and thus better understand the mechanisms that control it. This
concept is already in its early stages with today's efforts in formation flying of research and
operational satellites. The movement toward autonomous operations would allow many more
satellites to be controlled without substantially increasing the level of ground-based support.
WMO’s role
The potential impacts satellite systems will have on WMO programmes stand as a
lighthouse, with a very bright beacon. Satellite data are now continuously alerting to us a new
world with quantum leaps in potential for expanded capabilities for observations to support all
WMO programmes. The beacon is also a warning and increases in its intensity and repetition as
we approach. The future is bright from the perspective of the space agencies. Plans are in place
and technology will be providing tremendous possibilities. However, it is also recognized that a
single space agency cannot alone provide all the necessary resources to meet all of WMO
programmes’ requirements. A corollary to that recognition is that WMO, as a unique and focused
agency, must prepare for the future by acting as a catalyst for the development of international and
integrated satellite data and product dissemination and processing services. There are three
specific initiatives that WMO should systematically incorporate into its planning processes.
The first WMO initiative should be the establishment of an adequate data dissemination
service for the 2025 era. WMO should initiate the necessary framework to provide a dissemination
service whereby all the data from all satellites within the space-based component of the GOS
would be available to all WMO Members in a timely fashion. This will require joint participation by
all space agencies, and WMO Members. The underlying principle for the service should be
availability not delivery. All the data all the time to all Members would unduly constrain any
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delivery system. Availability of all data based on application areas and associated user’s
requirements would serve to outline an optimized service capacity.
The second WMO initiative would provide the basis for a global approach to product
development. It should be based on emerging technologies such as being demonstrated by the
Virtual Laboratory for Education and Training in Satellite Meteorology. WMO should embrace the
development of institutionalized programmes dedicated to producing specific and globally accepted
products needed by NMHSs, and national and international decision makers for all appropriate
application areas. The goal will be universally accepted products that can be prepared at
designated processing centres. This development initiative would be the research component of
the third initiative, a system of global processing centres for satellite products.
WMO Members would provide the resources for the third initiative at a regional level.
International processing centres for the “operational” products globally agreed-upon would
minimize duplication of processing power now found throughout many countries. Available
resources could remain focused on new product developments. The international and integrated
satellite data dissemination service would then make available the final products to WMO user.
By providing a basic suite of observations, international processing and global availability
on a timely basis, WMO Members would be able to fully exploit the potential from the satellite
systems of the next few decades. The opportunities better, more timely environmental information
can bring to society are tremendous. The challenge is before WMO.
ANNEX I
PHYSICAL BASIS OF REMOTE METHODS OF DETERMINING HYDRO-METEOROLOGICAL
AND GEOPHYSICAL PARAMETERS
Visible, Infrared and Microwave Spectral Bands for Viewing the Earth Atmosphere
Atmospheric Absorption and Emission of Solar Radiation
The absorption and emission of solar radiation in the atmosphere is accomplished by
molecular storing of the electromagnetic radiation energy. Molecules can store energy in various
ways. Any moving particle has kinetic energy as a result of its motion in space. This is known as
translational energy. A molecule which is composed of atoms can rotate, or revolve, around an
axis through its centre of gravity and, therefore, has rotational energy. The atoms of the molecule
are bounded by certain forces in which the individual atoms can vibrate about their equilibrium
positions relative to one another. The molecule, therefore, will have vibrational energy. These
three molecular energy types (translational, rotational, and vibrational) are based on a rather
mechanical model of the molecule that ignores the detailed structure of the molecule in terms of
nuclei and electrons. It is possible, however, for the energy of a molecule to change due to a
change in the energy state of the electrons of which it is composed. Thus, the molecule also has
electronic energy. The energy levels are quantized and take discrete values only. As we have
pointed out, absorption and emission of radiation takes place when the atoms or molecules
undergo transitions from one energy state to another. In general, these transitions are governed
by selection rules. Atoms exhibit line spectra associated with electronic energy levels. Molecules,
however, also have rotational and vibrational energy levels that lead to complex band systems.
Solar radiation is mainly absorbed in the atmosphere by O2, O3, N2, CO2, H2O, O, and N,
although NO, N2O, CO, and CH4, which occur in very small quantities, also exhibit absorption
spectra. Absorption spectra due to electronic transitions of molecular and atomic oxygen and
nitrogen, and ozone occur chiefly in the ultraviolet (UV) region, while those due to the vibrational
and rotational transitions of tri-atomic molecules such as H2O, O3, and CO2 lie in the infrared
region. There is very little absorption in the visible region of the solar spectrum. In Figure A1.1 the
transmittance of the visible and near infrared reflectance spectrum from 0.4 to 2.5 m is shown
along with the spectral band coverage of current and planned polar orbiting imagers.
Atmospheric Absorption and Emission of Thermal Radiation
Just as the sun emits electromagnetic radiation covering all frequencies, so does the
earth. However, the global mean temperature of the earth-atmosphere system is only about 250
K. This temperature is obviously much lower than that of the sun's photosphere. As a
consequence of Planck's law and Wien's displacement law discussed earlier, we find that the
radiance (intensity) peak of the Planck function is smaller for the earth's radiation field and the
wavelength for the radiance (intensity) peak of the earth's radiation field is longer. We call the
energy emitted from the earth-atmosphere system thermal infrared (or terrestrial) radiation. In
Figure A1.2, the earth radiance to space measured by an airborne interferometer is shown. We
plotted the spectral distribution of radiance emitted by a blackbody source at various temperatures
in the terrestrial range in terms of wavenumber. We saw that in some spectral regions the
envelope of the emission spectrum is very close to the spectrum emitted from a blackbody with a
temperature of about 295 K, which is about the temperature of the surface. This occurs in spectral
regions where the atmosphere is transparent to that radiation. In other spectral regions the
emission spectrum is close to the spectrum emitted from a blackbody with a temperature of about
220 K, which is about the temperature at the tropopause. This occurs in spectral regions where
the atmosphere is opaque or absorbing to that radiation. In these spectral regions the atmosphere
is said to be trapping the radiation. In Figure A1.3, the radiance from .1 μm to 10 cm emitted by
the earth-atmosphere system transmitted to space is shown. Clearly, certain portions of the
infrared radiation are trapped by various gases in the atmosphere.
ANNEX I, p. 2
Among these gases, carbon dioxide, water vapour, and ozone are the most important
absorbers. Some minor constituents, such as carbon monoxide, nitrous oxide, methane, and nitric
oxide, which are not shown, are relatively insignificant absorbers insofar as the heat budget of the
earth-atmosphere is concerned. Carbon dioxide absorbs infrared radiation significantly in the
15 μm band from about 600 to 800 cm-1. This spectral region also corresponds to the maximum
intensity of the Planck function in the wavenumber domain. Water vapour absorbs thermal infrared
in the 6.3 μm band from about 1200 to 2000 cm-1 and in the rotational band (< 500 cm-1). Except
for ozone, which has an absorption band in the 9.6 μm region, the atmosphere is relatively
transparent from 800 to 1200 cm-1. This region is referred to as the atmospheric window. In
addition to the 15 μm band, carbon dioxide also has an absorption band in the shorter wavelength
of the 4.3 μm region. The distribution of carbon dioxide is fairly uniform over the global space,
although there has been observational evidence indicating a continuous global increase over the
past century owing to the increase of the combustion of the fossil fuels. This leads to the question
of the earth's climate and possible climatic changes due to the increasing carbon dioxide
concentration. Unlike carbon dioxide, however, water vapour and ozone are highly variable both
with respect to time and the geographical location. These variations are vital to the radiation
budget of the earth-atmosphere system and to long-term climatic changes.
In a clear atmosphere without clouds and aerosols, a large portion (about 50%) of solar
energy transmits through the atmosphere and is absorbed by the earth's surface. Energy emitted
from the earth, on the contrary, is absorbed largely by carbon dioxide, water vapour, and ozone in
the atmosphere as evident in Figure A1.2. Trapping of thermal infrared radiation by atmospheric
gases is typical of the atmosphere and is, therefore, called the atmospheric effect.
Solar radiation is referred to as short-wave radiation because solar energy is concentrated
in shorter wavelengths with its peak at about 0.5 μm. Thermal infrared radiation from the earth's
atmosphere is referred to as long-wave radiation because its maximum energy is in the longer
wavelength at about 10 μm. The solar and infrared spectra are separated into two spectral ranges
above and below about 4 μm, and the overlap between them is relatively insignificant. This
distinction makes it possible to treat the two types of radiative transfer and source functions
separately and thereby simplify the complexity of the transfer problem.
Atmospheric Absorption Bands in the Infrared Spectrum
Inspection of high resolution spectroscopic data reveals that there are thousands of
absorption lines within each absorption band. The fine structure of molecular absorption bands for
the 320-380 cm-1 is due to water vapour, and for the 680-740 cm-1 region it is due to carbon
dioxide. The optically active gases of the atmosphere, carbon dioxide, water vapour, and ozone
are all tri-atomic molecules.
The water molecule forms an isosceles triangle that is obtuse. The 6.3 μm band has been
identified with a fundamental vibrational mode of H2O. Two other fundamental vibrational modes
are found close together in a band near 2.7 μm, i.e., on the short-wave side of the infrared spectral
region.
The band covering the region from 800 to 400 cm-1 shown in Figure A1.2 represents the
purely rotational spectrum of water vapour. The water molecule forms an asymmetrical top with
respect to rotation, and the line structure of the spectrum does not have the simplicity of a
symmetrical rotator such as found in the CO2 molecule. Close inspection shows that the
absorption lines have no clear-cut regularity. The fine structure of the 6.3 μm band is essentially
similar to that of the pure rotational band.
In the region between the two water vapour bands, i.e., between about 8 and 12 μm, the
so-called atmospheric window, absorption is continuous and is primarily due to water vapour.
Absorption by carbon dioxide is typically a small part of the total in this region. The overlap of
water vapour with ozone in this region is insignificant in the computations of cooling rates since
ANNEX I, p. 3
water vapour is important mainly in the lower atmosphere, while cooling due to ozone takes place
primarily in the stratosphere and higher.
The ozone molecule is of the tri-atomic non-linear type with a relatively strong rotation
spectrum. The three fundamental vibrational bands occur at wavelengths of 9.066, 14.27, and
9.597 μm. The very strong and moderately strong fundamentals combine to make the well-known
9.6 μm band of ozone. The other fundamental is well-masked by the 15 μm band of CO2. The
strong band of about 4.7 μm produced by the overtone and combination frequencies of 03
vibrations is in a weak portion of the Planck energy distribution for the atmosphere. Note that the
absorption bands of 03 in the UV part of the solar spectrum are due to electronic transitions in the
ozone molecule.
Atmospheric Absorption Bands in the Microwave Spectrum
A brief summary of the absorption lines in the microwave spectral region follows.
Molecular oxygen and water vapour are the major absorbing constituents here. Figure A1.4 shows
the transmittance for frequencies below 300 GHz. Below 40 GHz only the weakly absorbing
pressure broadened 22.235 GHz water vapour line is evident; this line is caused by transitions
between the rotational states. At about 60 and 118.75 GHz, there are strong oxygen absorption
lines due to magnetic dipole transitions. For frequencies greater than 120 GHz, water vapour
absorption again becomes dominant due to the strongly absorbing line at 183 GHz.
A special problem in the use of microwave from a satellite platform is the emissivity of the
earth surface. In the microwave region of the spectrum, emissivity values of the earth surface
range from 0.4 to 1.0. This complicates interpretation of terrestrial and atmospheric radiation with
earth surface reflections.
Remote Sensing Regions
Several spectral regions are considered useful for remote sensing from satellites.
Figure A1.5 summarizes this. Windows to the atmosphere (regions of minimal atmospheric
absorption) exist near 4 μm, 10 μm, 0.3 cm, and 1 cm. Infrared windows are used for sensing the
temperature of the earth surface and clouds, while microwave windows help to investigate the
surface emissivity and the liquid water content of clouds. The CO 2 and O2 absorption bands at 4.3
μm, 15 μm, 0.25 cm, and 0.5 cm are used for temperature profile retrieval; because these gases
are uniformly mixed in the atmosphere in known portions they lend themselves to this application.
The water vapour absorption bands near 6.3 μm, beyond 18 μm, near 0.2 cm, and near 1.3 cm are
sensitive to the water vapour concentration in the atmosphere.
Sounding the Atmosphere
Meteorological observations from space are made through the electromagnetic radiation
leaving the atmosphere. Outgoing radiation from earth to space varies with wavelength for two
reasons: (a) Planck function dependence on wavelength, and (b) absorption by atmospheric gases
of differing molecular structure (CO2, H2O, O3...). Figure A1.2 shows an observed infrared
spectrum of the radiance to space. Around absorbing bands of the constituent gases of the
atmosphere, vertical profiles of atmospheric parameters can be derived. Sampling in the spectral
region at the centre of the absorption band yields radiation from the upper levels of the atmosphere
(e.g., radiation from below has already been absorbed by the atmospheric gas); sampling in
spectral regions away from the centre of the absorption band yields radiation from successively
lower levels of the atmosphere. Away from the absorption band are the windows to the bottom of
the atmosphere. The interferometer on this day observed surface temperatures of 296 K in the 11
m window region of the spectrum and tropopause emissions of 220 K in the 15 m absorption
band. As the spectral region moves toward the centre of the CO2 absorption band, the radiation
temperature decreases due to the decrease of temperature with altitude in the lower atmosphere.
ANNEX I, p. 4
With careful selection of spectral bands in and around an absorbing band, it was
suggested that multispectral observations can yield information about the vertical structure of
atmospheric temperature and moisture. The concept of profile retrieval is based on the fact that
atmospheric absorption and transmittance are highly dependent on the frequency of the radiation
and the amount of the absorbing gas. At frequencies close to the centres of absorbing bands, a
small amount of gas results in considerable attenuation in the transmission of the radiation;
therefore most of the outgoing radiation arises from the upper levels of the atmosphere. At
frequencies far from the centres of the band, a relatively large amount of the absorbing gas is
required to attenuate transmission; therefore the outgoing radiation arises from the lower levels of
the atmosphere. However, the derivation of temperature profiles is complicated by the fact that
upwelling radiance sensed at a given wavelength arises from a rather large vertical depth (roughly
10 km) of the atmosphere. In addition, the radiance sensed in the neighbouring spectral regions
arises from deep overlapping layers. This causes the radiance observations to be dependent and
the inverse solution to the radiative transfer equation for temperature profiles to be non-unique.
Differing analytical approaches and types of ancillary data are needed to constrain the solution in
order to render temperature profiles.
There is no unique solution for the detailed vertical profile of temperature or an absorbing
constituent because (a) the outgoing radiances arise from relatively deep layers of the atmosphere,
(b) the radiances observed within various spectral channels come from overlapping layers of the
atmosphere and are not vertically independent of each other, and (c) measurements of outgoing
radiance possess errors. As a consequence, there are a large number of analytical approaches to
the profile retrieval problem. The approaches differ both in the procedure for solving the set of
spectrally independent radiative transfer equations (e.g., matrix inversion, numerical iteration) and
in the type of ancillary data used to constrain the solution to insure a meteorologically meaningful
result (e.g., the use of atmospheric covariance statistics as opposed to the use of an a priori
estimate of the profile structure). There are some excellent papers in the literature which review
the retrieval theory which has been developed over the past few decades (Fleming and Smith,
1971; Fritz et al, 1972; Rodgers, 1976; and Twomey, 1977).
There are several products that come under the category of soundings. They include the
clear field of view (FOV) brightness temperatures, profile retrievals of temperature and moisture, as
well as their layer mean values, lifted indices, CAPE, and thermal wind profiles. Additionally from
the imager, there are derived product images of precipitable water and lifted indices. A brief
description follows.
Vertical temperature profiles from sounder radiance measurements are produced at 41
pressure levels from 1000 to 0.1 hPa using a simultaneous, physical algorithm that solves for
surface skin temperature, atmospheric temperature and atmospheric moisture. Also, estimates of
surface emissivity and cloud pressure and amount are obtained as by products. The retrieval
begins with a first guess temperature profile that is obtained from a space/time interpolation of
fields provided by the numerical weather prediction models. Hourly surface observations are also
used to provide surface boundary information. Soundings are produced from an nxn array of
FOVs whenever more than 30% or more FOVs are determined to be either clear or "low cloud".
The FOVs are "cloud filtered" and co-registered to achieve an homogeneous set. The location
(latitude and longitude) of the retrieval is assigned to the mean position of the filtered sample. A
"type" indicator is included in the archive to indicate if the sounding represents "clear" or "low
cloud" conditions. A quality indicator is included to indicate if the retrieval has failed any internal
quality checks.
Vertical moisture (specific humidity) profiles are obtained in the simultaneous retrieval,
and thus are provided at the same levels as temperature. Since the radiance measurements
respond to the total integrated moisture above a particular pressure level, the specific humidity is a
differentiated quantity rather than an absolute retrieval. Geopotential height profiles are derived
from the full resolution temperature and moisture profiles. Layer means of either temperature or
moisture can also be derived. Ten or more precipitable water layers will be integrated from
ANNEX I, p. 5
retrievals of specific humidity. These and the total precipitable water are provided in the standard
archive.
Atmospheric stability indices (such as lifted index, CAPE, …) for each retrieval can also
be derived. The lifted index is an estimate of atmospheric stability that represents the buoyancy
that an air parcel would experience if mechanically lifted to the 500 hPa level. The lifted index
expresses the difference in temperature between the ambient 500 hPa temperature and the
temperature of the lifted parcel. A negative value (warmer than the environment) represents
positive buoyancy (continued rising); whereas a positive value denotes stability (returning descent).
The formulation used to derive LI is a thermodynamical relationship requiring the 500 hPa
temperature and a mean pressure, temperature, and moisture for the boundary layer. These
quantities are available from the retrieved profile. CAPE, another measure of atmospheric
instability, can also be provided.
The geopotential height of the pressure level as derived from a 1000 hPa height analysis
(from the NWP forecast supplemented with hourly data), a topography obtained from a library (10
minute latitude/longitude resolution) and the retrieved temperature and moisture profile are
contained in the archive of each retrieval. Thickness can be calculated from this profile.
Thermal winds can be provided with each profile. These are derived from objective
analyses of the geopotential profiles calculated with each retrieval. The objective analysis is a 3dimensional, univariate recursive filter that uses as a background the same fields that provide the
first guess to the temperature retrieval algorithm (NWP forecasts and surface analyses). The
analyses are currently performed on a 1 degree latitude/longitude grid. Gradient winds are
calculated using finite difference operators that involve surface-fitting over retrieval gridpoints
centred at the gridpoint closest to each retrieval. Wind estimates are provided from 700 to 400
hPa. These winds are most useful in the extra-tropics over water.
Vertical temperature and moisture (specific humidity) profiles are obtained in a
simultaneous physical retrieval (Ma et al. 1999). The overall quality of these products has been
assessed in case studies and comparison of information content with forecast model backgrounds.
Satellite sounder moisture contained in broad layers in the troposphere were compared to those
inferred from radiosonde measurements. The total column water vapour RMS difference with
respect to radiosondes for a one year period in 1996-97 has been reduced from 3.3 mm for the
forecast first guess to 2.6 mm for the satellite retrievals, roughly an improvement of 20%. It is
found that the satellite is typically drier than the radiosonde in the mean by 0.7 mm in the lowest
layer (surface to 900 hPa) and more moist in the mean by 0.3 mm in the middle (900 to 700 hPa)
as well as the upper (700 to 300 hPa) layers; the satellite improves upon the model first guess in
all layers in the RMS difference by 0.1 to 0.4 mm (Menzel et al. 1998). The inferred atmospheric
stability (such as lifted indices) of air parcels elevated to 500 hPa are found to be less stable in the
mean by 0.6 C from those inferred from radiosondes with a RMS difference of 2.2 C. In the vicinity
of radiosondes, the satellite depiction of atmospheric stability is improving upon numerical model
first guess information. More significant is the fact that much larger differences (greater than
100%) between satellite soundings and model forecasts often occur over oceanic regions where
radiosondes are unavailable; this indicates a much larger potential for satellite soundings to
influence the forecast model in data sparse regions.
Tracking Atmospheric Motions
Geostationary satellite imagery has been used as a source of wind observations since the
launch of the first spin scan camera aboard the Application Technology Satellite (ATS 1) in
December 1966. It was recognized immediately that features tracked in a time sequence of
images could provide estimates of atmospheric motion. Historically, wind vectors have been
produced from images of visible (for low level vectors) and infrared long-wave window radiation (for
upper and low level vectors and some mid level vectors). More recently, in order to improve the
ANNEX I, p. 6
coverage at mid levels and over cloud free areas, wind tracking has been applied to water vapour
imagery (at 6.7 and 7.2 m).
The basic elements of wind vector production have not changed since their inception.
These are: (a) selecting a feature to track or a candidate target; (b) tracking the target in a time
sequence of images to obtain a relative motion; (c) assigning a pressure height (altitude) to the
vector; and (d) assessing the quality of the vector. Initially, these elements were done manually
(even to the point of registering the images into a movie loop), but the goal has always been to
automate procedures and reduce the time consuming human interaction.
To use a satellite image, the feature of interest must be located accurately on the earth.
Since the earth moves around within the image plane of the satellite because of orbit effects,
satellite orbit and attitude (where the satellite is and how it is oriented in space) must be
determined and accounted for. This process of navigation is crucial for reliable wind vector
determination. With the assistance of landmarks (stars) to determine the attitude (orbit) of the
spacecraft over time, earth location accuracies within one visible pixel (one km) have been
realized.
Operational winds from GOES are derived from a sequence of three navigated and earth
located images taken 30 minutes apart. The winds are calculated by a three-step objective
procedure. The initial step selects targets, the second step assigns pressure altitude, and the third
step derives motion. Altitude is assigned based on a temperature/pressure derived from radiative
transfer calculations in the environment of the target. Motion is derived by a pattern recognition
algorithm that matches a feature within the "target area" in one image within a "search area" in the
second image. For each target two winds are produced representing the motion from the first to
the second, and from the second to the third image. An objective editing scheme is then employed
to perform quality control: the first guess motion, the consistency of the two winds, the precision of
the cloud height assignment, and the vector fit to an analysis are all used to assign a quality flag to
the "vector" (which is actually the average of the two vectors).
WVMVs (imager band at 6.7 m which sees the upper troposphere and sounder bands at
7.0 and 7.5 m which see deeper into the troposphere) are derived by the same methods used
with CMVs. Heights are assigned from the water vapour brightness temperature in clear sky
conditions and from radiative transfer techniques in cloudy regions.
The basic concept behind the cloud drift winds is that some clouds are passive tracers of
the atmosphere's motion in the vicinity of the cloud. However, clouds grow and decay with
lifetimes which are related to their size. To qualify for tracking, the tracer cloud must have a
lifetime that is long with respect to the time interval of the tracking sequence. The cloud must also
be large compared with the resolution of the images. This implies a match between the spatial and
temporal resolution of the image sequence. In order for a cloud to be an identifiable feature on an
image, it generally must occupy an area at least ten to 20 pixels across (where pixel denotes a
field of view). Hence for full resolution 1.0 km GOES visible data, the smallest clouds which can be
used for tracking are 10 to 20 km across. Experience has shown that a time interval of
approximately 3 to 10 min between images is necessary to track clouds of this size, with the
shorter time interval being required for disturbed situations. For 4 km infrared images, the cloud
tracers are about 100 km across, are tracked at half hour intervals, and represent an average
synoptic scale flow. Water vapour images are found to hold features longer and are best tracked
at hourly intervals (a longer time interval offers better accuracy of the tracer if the feature is not
changing).
Cloud drift winds have been compared to radiosondes and found to be within 5 to 8 m/s
rms (the better comparisons occur when the CO2 height algorithm is used for height assignment).
This is encouraging. However, it must be recognized that cloud winds are a limited and
meteorologically biased data set. The cloud winds generally yield measurements from only one
level (the uppermost layer of the cloud) and from regions where the air is going up (and producing
ANNEX I, p. 7
clouds). Even with the water vapour motions enhancing the cloud drift winds, the meteorological
bias persists. In summary, the satellite derived winds are best used over data sparse regions to fill
in some of the data gaps between radiosonde stations and between radiosonde launch times.
Investigating Clouds
Cloud Detection
Clouds are generally characterized by higher reflectance and lower temperature than the
underlying earth surface. As such, simple visible and infrared window threshold approaches offer
considerable skill in cloud detection. However there are many surface conditions when this
characterization of clouds is inappropriate, most notably over snow and ice. Additionally, some
cloud types such as cirrus, low stratus, and roll cumulus are difficult to detect because of
insufficient contrast with the surface radiance. Cloud edges cause further difficulty since the field
of view is not always completely cloudy or clear. Multispectral approaches offer several
opportunities for improved cloud detection so that many of these concerns can be mitigated.
Finally, spatial and temporal consistency tests offer confirmation of cloudy or clear sky conditions.
The purpose of a cloud mask is to indicate whether a given view of the earth surface is
unobstructed by clouds. The question of obstruction by aerosols is somewhat more difficult and
will be addressed only in passing in this section. As many as eight single field of view (FOV) cloud
mask tests are indicated for daylight conditions (given that the sensor has the appropriate spectral
channels). Many of the single FOV tests rely on radiance (temperature) thresholds in the infrared
and reflectance thresholds in the visible. These thresholds vary with surface emissivity,
atmospheric moisture, aerosol content, and viewing scan angle.
(a)
IR Window Temperature Threshold and Difference Tests
Several infrared window threshold and temperature difference techniques are practical.
Thresholds will vary with moisture content of the atmosphere as the long-wave infrared windows
exhibit some water vapour absorption. Threshold cloud detection techniques are most effective at
night over water. Over land, the threshold approach is further complicated by the fact that the
emissivity in the infrared window varies appreciably with soil and vegetation type. Over open
ocean when the brightness temperature in the 11 m channel (Tb11) is less than 270 K, we can
safely assume a cloud is present. As a result of the relative spectral uniformity of surface
emittance in the IR, spectral tests within various atmospheric windows (such as those at 8.6, 11,
and 12 m respectively) can be used to detect the presence of a cloud. Differences between T b11
and Tb12 have been widely used for cloud screening with AVHRR measurements and this
technique is often referred to as the split window technique.
The basis of the split window technique for cloud detection lies in the differential water
vapour absorption that exists between the window channels (8.6 and 11 m and 11 and 12 m).
These spectral regions are considered to be part of the atmospheric window, where absorption is
relatively weak. Most of the absorption lines are a result of water vapour molecules, with a
minimum occurring around 11 m. Since the absorption is weak, Tb11 can be corrected for
moisture absorption by adding the scaled brightness temperature difference of two spectrally close
channels with different water vapour absorption coefficients; the scaling coefficient is a function of
the differential water vapour absorption between the two channels. This approach has been used
operationally for 6 years using 8.6 and 11 m bandwidths from the NOAA-10 and NOAA-12 and
the 11 and 12 m bandwidths from the NOAA-11, with a coefficient independent of PW.
A disadvantage of the split window brightness temperature difference approach is that
water vapour absorption across the window is not linearly dependent on PW, thus second order
relationships are sometimes used. With the measurements at three wavelengths in the window,
8.6, 11 and 12 m this becomes less problematic. The three spectral regions mentioned are very
ANNEX I, p. 8
useful in determination of a cloud free atmosphere. This is because the index of refraction varies
quite markedly over this spectral region for water, ice, and minerals common to many naturally
occurring aerosols. As a result, the effect on the brightness temperature of each of the spectral
regions is different, depending on the absorbing constituent.
A tri-spectral combination of observations at 8.6, 11 and 12 m bands has been used
recently for detecting cloud and cloud properties. The premise of the technique is that ice and
water vapour absorption is larger in the window region beyond 10.5 m; so that positive 8.6 minus
11 m brightness temperature differences indicate cloud while negative differences, over oceans,
indicate clear regions. The relationship between the two brightness temperature differences and
clear sky have also been examined using collocated HIRS and AVHRR GAC global ocean data
sets. As the atmospheric moisture increases, Tb8.6 - Tb11 decreases while Tb11 - Tb12 increases.
The short-wave infrared window channel at 3.9 m also measures radiances in another
window region near 3.5 - 4 m so that the difference between Tb11 and Tb3.9 can also be used to
detect the presence of clouds. At night the difference between the brightness temperatures
measured in the short-wave (3.9 m) and in the long-wave (11 m) window regions Tb3.9-Tb11 can
be used to detect partial cloud or thin cloud within the sensor field of view. Small or negative
differences are observed only for the case where an opaque scene (such as thick cloud or the
surface) fills the field of view of the sensor. Negative differences occur at night over extended
clouds due to the lower cloud emissivity at 3.9 m. Moderate to large differences result when a
non-uniform scene (e.g., broken cloud) is observed. The different spectral response to a scene of
non-uniform temperature is a result of Planck's law; the brightness temperature dependence on the
warmer portion of the scene increasing with decreasing wavelength (the short-wave window
Planck radiance is proportional to temperature to the thirteenth power, while the long-wave
dependence is to the fourth power). Differences in the brightness temperatures of the long-wave
and short-wave channels are small when viewing mostly clear or mostly cloudy scenes; however
for intermediate situations the differences become large. The brightness temperature of the shortwave window channel is relatively insensitive to small amounts of cloud (compared to the longwave window channel), thus making it the preferred channel for surface temperature
determinations.
Cloud masking over land surface from thermal infrared bands is more difficult than ocean
due to potentially larger variations in surface emittance. Nonetheless, simple thresholds can be
established over certain land features. For example, over desert regions we can expect that T b11
< 273 K indicates cloud. Such simple thresholds will vary with the ecosystem, season and time of
day and are still under investigation. Brightness temperature difference testing can also be applied
over land with careful consideration of variation in spectral emittance. For example, T b11 - Tb8.6 has
large negative values over daytime desert and is driven to positive differences in the presence of
cirrus. Some land regions have an advantage over the ocean regions because of the larger
number of surface observations, which include air temperature, and vertical profiles of moisture
and temperature.
Infrared window tests at high latitudes are difficult. Distinguishing clear and cloud regions
from satellite IR radiances is a challenging problem due to the cold surface temperatures. One
cloud/surface discrimination algorithm is based upon brightness temperature differences between
the AVHRR 3.7 and 11 m channels and between the 11 and 12 m channels. This cloud/surface
discrimination algorithm is more effective over water surfaces than over inland snow-covered
surfaces. A number of problems arse over inland snow-covered surfaces. First, the temperature
contrast between the cloud and snow surface becomes especially small, leading to a small
brightness temperature difference between the two infrared channels. Second, the AVHRR
channels are not well-calibrated at extremely cold temperatures (< 200 K). Under clear sky
conditions, surface radiative temperature inversions often exists. Thus, IR channels whose
weighting function peaks down low in the atmosphere, will often have a larger brightness
temperature than a window channel. For example Tb8.6 > Tb11 in the presence of an inversion. The
ANNEX I, p. 9
surface inversion can also be confused with thick cirrus cloud, but this can be mitigated by other
tests (e.g., the magnitude of Tb11 or Tb11-Tb12). Recent analysis of Tb11-Tb6.7 (the 6.7 m water
vapour channel peaks around 400 hPa) has shown large negative difference in winter time over
the Antarctic Plateau and Greenland, which may be indicative of a strong surface inversion and
thus clear skies.
(b)
Reflectance Tests
Visible threshold tests are best used in combination with infrared window observations;
during daytime they can be combined as follows. Low reflectance measurements will result from
thin cirrus cloud or cloud free conditions, the two being easily separable in the infrared window
measurements by the large difference in the emitting temperature of the high cold cirrus and the
warm underlying surface. High reflectance measurements result from thick clouds at all levels, and
the infrared window brightness temperature provides a good indication of the cloud level.
Intermediate reflectance data are subject to ambiguous interpretations since they result from a
mixture of cloud and surface contributions. Visible data used in the determination of the cloud
mask must be uncontaminated by sun glint.
The reflectance ratio takes advantage of the difference in reflection from cloud versus
earth surface in wavelengths above and below 0.75 m. Many earth surfaces are less reflecting
below 0.75 m than above (which is the basis of the vegetation index) , but clouds do not exhibit
any great difference in reflectance. One version of the reflectance ratio test can use the 0.87 m
reflectance divided by 0.66 m reflectance (r.87/r.66). With AVHRR data, this ratio has been found
to be between 0.9 and 1.1 in cloudy regions. If the ratio falls within this range, cloud is indicated.
New analyses suggest that the minimum value may need to be lowered to about 0.8, at least for
some cases. For cloud-free ocean, the ratio is expected to be less than 0.75.
Clouds that are low in the atmosphere are often difficult to detect. The thermal contrast
between clear sky and low cloud is small and sometimes undetectable by infrared techniques.
Reflectance techniques, including the Reflectance Ratio Test can be applied during daylight hours.
Use of a channel at 0.936 m also offers help under daytime viewing conditions; this channel is
strongly affected by low level moisture. When low clouds are present they obstruct the low level
moisture, hence increasing the reflectance. A reflectance ratio of 0.936 over 0.865 m, an
atmospheric window with similar surface reflectance characteristics, also shows promise.
(c)
Microwave Tests
The brightness temperature of a lower tropospheric sounding microwave channel can be
regressed against the brightness temperatures of several lower tropospheric infrared sounding
channels for clear situations. Therefore, the microwave brightness temperature for a given FOV
can be calculated from the observed infrared brightness temperatures. This will be valid in clear
sky conditions only. If the observed microwave brightness temperature is greater than the
calculated, it is indicative of cloud contamination in the infrared observations and the FOV should
be classified accordingly.
(d)
Resultant Cloud Mask
All of the single pixel tests mentioned so far rely on thresholds. Thresholds are never
global. There are always exceptions and the thresholds must be interpreted carefully. As one
approaches the threshold limits, the certainty or confidence in the labelling becomes more and
more uncertain. An individual confidence flag must be assigned and used with the single pixel test
results to work towards a final determination.
Each threshold determination is “pass”, “conditional pass”, or “fail” along with a confidence
assessment. Conditional pass involves those radiances that fall within an uncertainty region of the
ANNEX I, p. 10
threshold. The uncertainty is a measure of instrument noise in that channel and the magnitude of
the correction due to non-blackbody surface emissivity as well as atmospheric moisture and/or
aerosol reflection contributions. The individual confidence flag indicates a one, two, or three sigma
confidence level for each single pixel test result. The initial FOV obstruction determination is a
sum of the squares of all the confidence flags and single pixel test results.
(e)
Spatial Uniformity Tests To Find Cloud
When the single field of view tests do not definitively determine an unobstructed FOV,
spatial and temporal consistency tests are often useful. Temporal consistency compares
composite previous 30 day clear sky radiances and yesterday's cloud mask to today's clear sky
single pixel results. Spatial consistency checks neighbouring clear sky pixel radiances (same
ecosystem). If any consistency test fails, the confidence in the final cloud/no cloud determination is
reduced by 1 sigma level.
Cloud Properties
CO2 slicing has been used to distinguish transmissive clouds from opaque clouds and
clear sky using High resolution Infrared Radiation Sounder (HIRS) multispectral observations.
With radiances around the broad CO2 absorption band at 15 m, clouds at various levels of the
atmosphere can be detected. Radiances from near the centre of the absorption band are sensitive
to only upper levels while radiances from the wings of the band (away from the band centre) see
successively lower levels of the atmosphere. The CO2 slicing algorithm determines both cloud
level (and hence the associated cloud temperature) and cloud amount from radiative transfer
principles. It has been shown to be especially effective for detecting thin cirrus clouds that are
often missed by simple infrared window and visible approaches. Difficulties arise when the
spectral cloud forcing (clear minus cloudy radiance for a spectral band) is less than the instrument
noise.
Sensing the Earth Surface
Temperature
Sea surface temperatures (SST) are derived using cloud free measurements in the
infrared window with varying sensitivity to atmospheric moisture; this is often referred to as the split
window technique. SSTs have been measured from satellites for nearly two decades by the
Advanced Very High Resolution Radiometer (AVHRR) and, more recently, by the Along Track
Scanning Radiometer (ATSR). Both series of satellites operate from sun-synchronous polar orbits
about 850 km above ground. Compared with in situ measurements by ship and buoy, a great
advantage of these satellite SST measurements is their global, nearly uniform coverage (except for
clouds) with high spatial resolution (1 km). Significant progress has been made in meteorology,
climatology, oceanography, and other branches of geoscience using the AVHRR long term record
of high quality SST estimates.
Since 1993, GOES SST estimates have also been possible. Geostationary advantages
are frequent sampling which results in a more complete map of SST as clouds move away.
Changes in the scene temperature over a short period of time help to detect the presence clouds.
The abundance of GOES observations enables stringent screening for cloud free observations
while maintaining good spatial coverage of clear sky inferences of SST. Diurnal variations of SST
over large areas are observed for the first time and their implications for numerical weather
prediction and climate monitoring are being studied.
An integral part of the SST algorithm is the detection of cloud contamination. Correction
for atmospheric water vapour absorption and re-emission is done with simple regression of
radiance against buoy measurement. Most current SST algorithms do not treat explicitly, among
other things, the effects of aerosol, non-blackbody sea surface, and the difference between
ANNEX I, p. 11
satellite and buoy measurements (an area estimate of skin SST versus a point estimate of bulk
SST). Nevertheless, agreement between satellite and buoy SST reports is within 0.6 C; better
results are expected with improved multispectral cloud detection and accounting for reflection from
the ocean surface.
Over land, accounting for the surface emissivity is critical. There is good experimental
evidence that high spectral resolution infrared measurements (from interferometers and grating
spectrometers) will enable determination of surface emissivity as well as temperature. The
algorithm utilizes infrared window measurements that resolve on-absorption line and off-absorption
line water vapour features to derive a surface temperature that minimizes the on-line and off-line
surface emissivity variations.
Moisture
Satellite remote sensing of soil moisture has not been very successful. Visible and
infrared remote sensing see only the very surface layer of soil or of the vegetation canopy above
the soil. The wetness of the surface may affect the reflected radiation and will certainly affect the
soil temperature, but the changes caused by surface wetness are difficult to quantify and
distinguish from other physical phenomena that can change soil brightness or temperature. To
observe soil moisture over any reasonable soil depth (say 5 to 15 cm) requires remote sensing
from microwave radiation. Microwave window bands can retrieve information from below the soil
surface at a depth that is comparable to the wavelength of the microwave radiation. As the
microwave instruments sample to longer and longer wavelength, which is required to measure soil
moisture at significant depth, the antenna on the satellite has to increase in size and the field of
view at the surface gets larger. To date, practical limitations on satellite antenna size has
restricted microwave observations to 1 cm wavelength or less. This has allowed observation of
surface soil moisture or surface wetness only. A further problem of microwave soil moisture is that
overlaying vegetation interferes with the soil signal. This limitation restricts observations to less
thickly vegetated regions or confines the signal to situations where there is a very large soil
moisture signal, such as occurs under flood conditions or with extensive ponding after heavy rains.
Vegetation
AVHRR measures reflected visible radiation in the 0.58 – 0.68 µm band (channel 1) and
the 0.7 – 1.1 µm band (channel 2) of the Earth’s reflected radiation. This band pair, when
combined in a quantity called Normalized Difference Vegetation Index (NDVI) has the property of
being very sensitive to vegetation density and vigour. The NDVI is defined by:
NDVI = (Ch2 – Ch1)/(Ch2 + Ch1)
where the Ch1 and Ch2 values can be expressed in terms of albedo or reflectance. The
reflectance of green vegetation is generally low in the red part of the spectrum (Channel 1) and
high in the near infrared (Channel 2) regions. As observed vegetation becomes senescent, in poor
health, or sparse, the near infrared reflectance (Channel 2) declines and the NDVI decreases. So
high values of NDVI denote dense green healthy vegetation. Low NDVI usually indicates stressed
vegetation or scenes (arid and semiarid or wintertime) where the amount of green vegetation is
low.
Since 1985 the AVHRR NDVI has been used to make weekly global maps of vegetation
index as a means of routinely monitoring world-wide vegetation condition. Vegetation index maps
are produced daily, then combined by compositing on the maximum vegetation index observed
during the week, to provide a substantially cloud-free vegetation map at the end of the week. The
weekly maps are the basis for a variety of other vegetation-related products such as stressed
vegetation caused by drought and green vegetation fraction that is used to specify surface
conditions in numerical weather prediction models.
ANNEX I, p. 12
Fires
The most practical and economically feasible manner of monitoring the extent of burning
associated with tropical deforestation and grassland management is through remote sensing. To
date, many remote sensing methods have utilized multispectral data from the Multispectral
Scanner (MSS) on Landsat-1, -2,. ., -5, the Thematic Mapper (TM) on Landsat-4 and -5, and the
Advanced Very High Resolution Radiometer (AVHRR) on the NOAA polar orbiters. A number of
these techniques calculate vegetative indices (from measurements above and below the
vegetation reflectance step function at 0.72 m) in order to estimate deforestation areas.
However, the extent of deforestation is usually underestimated, mostly due to the inability to
distinguish between primary and secondary growth
Another estimation of the rate of deforestation can be made by monitoring biomass
burning. A technique utilizing the AVHRR 3.7 m and 10.8 m channels to detect subpixel
resolution forest fires has been used successfully. The technique provides reasonable estimates
of temperature and area of fires in 1 km pixels that are not saturated. Unfortunately, many of the
pixels are saturated and it is difficult to monitor plume activity associated with these sub-pixel fires,
since the NOAA polar orbiting satellite has only one day time pass over a given area. The
Geostationary Operational Environmental Satellites offer continuous viewing and less pixel
saturation. Furthermore, the fire plumes can be tracked in time to determine their motion and
extent. Thus the GOES satellite offers a unique ability to monitor diurnal variations in fire activity
and transport of related aerosols.
The different brightness temperature responses in the two infrared window channels can
be used to estimate the temperature of the target fire as well as the sub-pixel area it covers.
Typically, the difference in brightness temperatures between the two infrared windows at 3.9 and
11.2 m is due to reflected solar radiation, surface emissivity differences, and water vapour
attenuation. This normally results in brightness temperature differences of 2 - 4 K. Larger
differences occur when one part of a pixel is substantially warmer than the rest of the pixel. The
hotter portion will contribute more radiance in shorter wavelengths than in the longer wavelengths.
The fire extent and temperature within a field of view can be determined by considering the
upwelling thermal radiance values obtained by both channels. The observed short-wave window
radiance also contains contributions due to solar reflection that must be distinguished from the
ground emitted radiances; solar reflection is estimated from differences in background
temperatures in the 4 and 11 m channels. Once background temperature is estimated from
nearby pixels, atmospheric correction for moisture and smoke is accomplished, and surface
emissivity adjustments are made for short-wave and long-wave IR radiation, two equations and two
unknowns (fire extent and temperature) result. Burning or smouldering fires are usually covered by
clouds and smoke containing organic particles of varying sizes and shapes, necessitating a
correction to the transmittance. Most of the smoke is composed of water vapour, but there are
other constituents as well. The 11 m channel is more affected by atmospheric water vapour than
the 4 m channel. With Nimbus-2 data, it was found that the water vapour correction for a moist
atmosphere is approximately 4 K at 300 K for the 11 m window and 2 K at 300 K for the 4 m
window. By calculating a linear regression relationship between the GOES visible brightness
counts and GOES infrared window brightness temperature in a variety of haze conditions
(approximately 50) and extrapolating to clear sky conditions, the Nimbus corrections were found to
be appropriate for the GOES data studied. Emissivity investigations for vegetation similar to that
found in the selva and cerrado suggest an emissivity for tropical rainforest of 0.96 in the 4 m
region and 0.97 in the 11 m region, while the emissivity of dry grassland is .82 and .88
respectively.
ANNEX I, p. 13
O3
O2
H2O
H2O
H2O
H2O CO2
H2O
Figure A1.1: The transmittance through the atmosphere of the visible and near infrared
reflectance spectrum from 0.4 to 2.5 m (spectral absorption gases are indicated below) is shown
along with the spectral band coverage of current and planned polar orbiting imagers.
VIIR
S
MODIS
CO2
H2O
O3
CO2
Figure A1.2: Infrared portion of the earth-atmosphere emitted radiation to space observed from an
airborne Interferometer. Planck envelopes and line spectra (absorption gases are indicated below)
as well as spectral band coverage of polar imagers MODIS and VIIRS are indicated. Sounders
cover CO2 and H2O absorption bands and window regions.
ANNEX I, p. 14
Figure A1.3: Atmospheric transmission characteristics from 0.1 m to 10 cm showing the major
absorption bands.
Figure A1.4: Atmospheric transmittance in the microwave region of the spectrum as a function of
frequency.
ANNEX I, p. 15
Figure A1.5: Spectral regions used for remote sensing of the earth atmosphere and surface from
satellites.  indicates emissivity, q denotes water vapour, and T represents temperature.
ANNEX II
OBSERVATIONAL REQUIREMENTS FOR WMO PROGRAMMES
User rqmts Max/Min
31-Jan-01
Application
Requirement
Hor
Res
Min
Vert
Res
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
12 h
12 h
12 h
12 h
30 d
10 y
365 d
1500 d
24 h
7d
12 h
5d
1d
12 h
24 h
24 h
24 h
24 h
30 d
15 y
1500 d
30 d
48 h
7d
24 h
7d
5d
24 h
24 h
24 h
24 h
24 h
90 d
30 d
30 d
90 d
720 h
30 d
24 h
30 d
7d
720 h
48 h
10 %
48 h
0.2 K
48 h
15 %
48 h
0.5 km
1m
2m
90 d
5 cm (vert.)
90 d
10 % (Max)
1m
3m
1440 h 0.5 K
5 % (Max)
48 h
1K
10 % (Max)
30 d
5 mm
1440 h 1 m/s
20 %
0.5 K
30 %
1 km
Tentative
10 cm
20 %
Tentative
2K
10 %
2K
20 %
20 mm
5 m/s
Tentative
Tentative
Reasonable
Reasonable
WCRP
Tentative
Tentative
WCRP
Reasonable
Reasonable
Reasonable
Reasonable
Tentative
Reasonable
3h
3h
3h
3h
3h
3h
3h
1h
1h
1h
1h
1h
1h
1h
2h
2h
2h
2h
2h
2h
2h
5K
30 µm
25 %
25 %
25 %
25 %
10 %
Firm
Firm
Firm
Firm
Firm
Firm
Firm
Remarks
Source
Min
ACSYS
Air specific humidity (at surface)
Air temperature (at surface)
Cloud optical thickness
Cloud top height
Ice thickness 200 km
Ice-sheet topography
Iceberg fractional cover
Iceberg height 1 km
Sea surface temperature
Sea-ice cover 25 km
Sea-ice surface temperature
Snow cover
1 km
Snow water equivalent
Wind vector over sea surface (horizontal)
100 km
100 km
100 km
100 km
500 km
0.1 km
1 km
10 km
25 km
100 km
100 km
25 km
10 km
25 km
500 km
500 km
500 km
500 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
100 km 0.15
100 km
100 km 0.15
100 km 0.15
100 km 0.15
100 km 0.15
100 km 0.15
7d
0.5 km
10 km
365 d
100 km
1d
500 km
1d
25 km
100 km
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
Aero Met
Atmospheric temperature profile - Lower troposphere (LT)
Cloud drop size (at cloud top)
Cloud ice profile - Higher troposphere (HT)
Cloud ice profile - Lower troposphere (LT)
Cloud water profile (< 100 µm) - Lower troposphere (LT)
Cloud water profile (> 100 µm) - Lower troposphere (LT)
Specific humidity profile - Lower troposphere (LT)
0.6 km 1 h
1h
0.6 km 1 h
0.6 km 1 h
0.6 km 1 h
0.6 km 1 h
0.6 km 1 h
2K
15 µm
10 %
10 %
10 %
10 %
5%
gnd - Fl 260
WMO
WMO
WMO
WMO
WMO
WMO
WMO
ANNEX II, p. 2
Application
Requirement
Hor
Res
Min
Vert
Res
Wind profile (horizontal component) - Higher troposphere
50 km
100 km 0.15
0.6 km 0.0833 0.167 h 1 h
3h
2 m/s
5 m/s
Firm
Near steep topography or jet
streams min requirements
for vertical gradient
information
5m/s1000ft
WMO
Wind profile (horizontal component) - Lower stratosphere
50 km
100 km 0.15
0.6 km 0.0833 0.167 h 1 h
3h
2 m/s
5 m/s
Firm
Near steep topography or jet
streams min requirements
for vertical gradient
information
5m/s1000ft
WMO
Wind profile (horizontal component) - Lower troposphere
50 km
100 km 0.15
0.6 km 0.0833 0.167 h 1 h
3h
2 m/s
5 m/s
Firm
Near steep topography or jet
streams min requirements
for vertical gradient
information
5m/s1000ft
WMO
Fire area
0.01
10 km
0.25 d 1 d
0.0416 0.25 d 10 %
20 %
Reasonable
Saturation lvl not reached
before fire detected
WMO
Fire temperature
0.01
10 km
0.25 d 1 d
0.0416 0.25 d 50 K
200 K
Reasonable
Saturation lvl not reached
before fire detected
WMO
Land cover
1000 m
2y
10 d
30 d
10 classes
4 classes
Reasonable
Land utilization map
composition
WMO
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
Remarks
Source
Min
Agricultural Meteorology
100 m
1y
Land surface temperature
0.1 km 10 km
1h
72 h
3h
24 h
0.3 K
2K
Reasonable
Detection of areas affected by
frost; drought eval
WMO
Leaf Area Index (LAI)
0.01
10 km
5d
7d
1d
5d
5 % (Max)
10 %
Reasonable
Evapotranspiration
estimation; crop productivity
WMO
Normalized Differential Vegetation Index (NDVI)
1 km
10 km
1d
7d
1d
5d
5 % (Max)
10 %
Reasonable
Event detection; crop state
estimation
WMO
Photosynthetically Active Radiation (PAR)
5 km
100 km
0.0416 7 d
1d
5d
10 W/m2
50 W/m2
Reasonable
Photosynthesis estimation;
crop procductivity eval
WMO
Precipitation index (daily cumulative)
10 km
50 km
24 h
72 h
24 h
48 h
2 mm/d
10 mm/d
Reasonable
Evaluation of soil moisture
and avail to plants
WMO
Snow cover
1 km
10 km
5d
7d
1d
5d
2 % (Max)
10 %
Reasonable
Evaluation of crop wintering
WMO
Snow water equivalent
1 km
10 km
7d
30 d
1d
7d
5 mm
500 mm
Reasonable
Evaluation of soil water
storage in spring
WMO
ANNEX II, p. 3
Application
Requirement
Soil moisture
Soil type
Hor
Res
Min
Vert
Res
Min
0.1 km 1 km
0.1 km
Obs
Cycle Min
Delay
avail Min
Accuracy
1d
7d
1d
5d
10 g/kg
Confidence
Remarks
Source
Min
50 g/kg
Reasonable
Crop state and productivity
estimation
WMO
10 km
1y
2y
10 d
30 d
15 classes
5 classes
Reasonable
Crop state estimation, soil
moisture evaluation
WMO
500 m
30 d
60 d
1d
7d
30 classes
5 classes
Reasonable
Land utilization map
composition; crop eval
WMO
Atmospheric temperature profile - Higher stratosphere &
mesosphere (HS & M)
100 km 500 km 2 km
3 km
3h
6h
3h
12 h
1K
3K
Firm
Signal size in B.D. Santer et
al., 1999 JGR, 104,
6305-6333, supports this
need.
GCOS
Atmospheric temperature profile - Higher troposphere (HT)
100 km 500 km 0.1 km 0.5 km 3 h
6h
3h
12 h
0.5 K
2K
Firm
Signal size in B.D. Santer et
al., 1999 JGR, 104,
6305-6333, supports this
need.
GCOS
Atmospheric temperature profile - Lower stratosphere (LS)
100 km 500 km 0.1 km 0.5 km 3 h
6h
3h
12 h
0.5 K
2K
Firm
Signal size in B.D. Santer et
al., 1999 JGR, 104,
6305-6333, supports this
need.
GCOS
Atmospheric temperature profile - Lower troposphere (LT)
100 km 500 km 0.1 km 2 km
3h
6h
3h
12 h
0.5 K
2K
Firm
Signal size in B.D. Santer et
al., 1999 JGR, 104,
6305-6333, supports this
need.
GCOS
Cloud cover
500 km
6h
3h
12 h
10 % (Max)
20 %
Firm
Accuracies should apply to
low and high clouds
separately. See Hansen et
a (1995) in T.R. Karl (Ed.)
1995 Long term climate
monitoring by the Global
Climate Observing System.
Kluwer. Also, Climatic
Change 31, Nos. 2-4.
GCOS
3h
6h
3h
12 h
Missing
Firm
Vegetation type 50 m
AOPC
100 km
Cloud ice profile - Total column
100 km 500 km
3h
Missing
GCOS
ANNEX II, p. 4
Application
Requirement
Hor
Res
Min
Vert
Res
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
Remarks
Source
Min
Cloud top height
100 km 500 km
3h
6h
3h
12 h
0.5 km
2 km
Firm
Accuracy should be 5hPa
(10hPa - Min). Hansen et al.
(1995) in T.R. Karl (Ed.)
1995 Long term climate
monitoring by the Global
Climate Observing System.
Kluwer. Also, Climatic
Change 31, Nos. 2-4.
GCOS
Cloud top temperature
100 km 500 km
3h
6h
3h
12 h
0.3 K
0.6 K
Firm
Hansen et al. (1995) in T.R.
Karl (Ed.) 1995 Long term
climate monitoring by the
Global Climate Observing
System. Kluwer. Also,
Climatic Change
31, Nos. 2-4.
GCOS
Cloud water profile (< 100 µm) - Total column
Cloud water profile (> 100 µm) - Total column
100 km 500 km
100 km 500 km
3h
3h
6h
6h
3h
3h
12 h
12 h
Missing
Missing
Missing
Missing
Firm
Firm
0.125 d 7 d
0.125
1d
1 W/m2
2 W/m2
Firm
See Hansen et al. (1995) in
T.R. Karl (Ed.) 1995 Long
term climate monitoring by
GCOS. Kluwer. Also,
Climatic Change 31, Nos. 24. Climate forcing by strat
O3, need low bias &
spectrally-resolved
(~1nm) solar irradiance. UV
monitoring may need higher
freq.
GCOS
3K
Firm
Signal size supports this need.
See P.D. Jones et al. 1999.
Surface air temperature and
its changes over the past
150 years. Rev. Geophys.,
in press.
GCOS
Downwelling solar radiation at TOA
Land surface temperature
100 km 500 km
3h
6h
3h
6h
1K
Normalized Differential Vegetation Index (NDVI)
100 km 500 km
168 d
720 d
10 d
30 d
10 % (Max) 20 %
Firm
GCOS
GCOS
GCOS
ANNEX II, p. 5
Application
Requirement
Obs
Cycle Min
Delay
avail Min
Accuracy
200 km 500 km
3h
6h
3h
24 h
5 W/m2
10 W/m2
Firm
Accuracy is consistent with
Hansen et al. (1995) in T.R.
Karl (Ed.) 1995 Long term
climate monitoring by the
Global Climate Observing
System. Kluwer. Also,
Climatic Change 31, Nos. 2-
GCOS
Outgoing short-wave radiation at TOA
200 km 500 km
3h
6h
3h
24 h
5 W/m2
10 W/m2
Firm
Accuracy is consistent with
Hansen et al. (1995) in T.R.
Karl (Ed.) 1995 Long term
climate monitoring by the
Global Climate Observing
System. Kluwer. Also,
Climatic Change 31, Nos. 24.
GCOS
Precipitation rate at the ground (liquid)
100 km 500 km
3h
6h
3h
12 h
0.6 mm/h
2 mm/h
Firm
GCOS
Precipitation rate at the ground (solid)
100 km 500 km
3h
6h
3h
12 h
0.6 mm/h
2 mm/h
Firm
GCOS
Sea surface temperature
100 km 500 km
24 h
72 h
3h
12 h
0.3 K
1K
Firm
Signal size supports this need.
See P.D. Jones et al. 1999.
Surface air temperature and
its changes over the past
150 years. Rev. Geophys.,
in press.
GCOS
Sea-ice cover 100 km
500 km
7d
0.125
1d
10 % (Max)
20 %
Firm
Decadal changes may be of
order 5% (Walsh, 1995 in
T.R. Karl (Ed.) 1995 Long
term climate monitoring by
the Global Climate
Observing System. Kluwer.
Also, Climatic Change 31,
Nos. 2-4.)
GCOS
Significant wave height
100 km 250 km
3h
6h
3h
12 h
2m
Firm
Outgoing long-wave radiation at TOA
Hor
Res
Min
Vert
Res
Min
Confidence
Remarks
Source
Min
4.
1d
0.5 m
GCOS
ANNEX II, p. 6
Application
Requirement
Snow cover
100 km
Hor
Res
Min
Vert
Res
500 km
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
1d
7d
0.125
1d
10 % (Max)
20 %
Firm
1d
7d
0.125
1d
5 mm
10 mm
Firm
Remarks
Source
Min
Decadal changes may be of
order 5% (N. Nicholls et al.
1996 Observed Climate
Variability and Change.
Chapter 2 of Climate
Change
1995 (IPCC
Second Assessment).
GCOS
Snow water equivalent
100 km 500 km
GCOS
Specific humidity profile - Higher stratosphere &
mesosphere (HS & M)
100 km 500 km 1 km
3 km
3h
6h
3h
12 h
5%
10 %
Firm
Signal sizes in section 3.3.7 of
N. Nicholls et al. 1996
Observed Climate Variability
and Change. Chapter 2 of
Climate Change 1995 (IPCC
Second Assessment).
support this need.
GCOS
Specific humidity profile - Higher troposphere (HT)
100 km 500 km 0.5 km 1 km
3h
6h
3h
12 h
5%
10 %
Firm
Signal sizes in section 3.3.7 of
N. Nicholls et al. 1996
Observed Climate Variability
and Change. Chapter 2 of
Climate Change 1995 (IPCC
Second Assessment).
support this need.
GCOS
Specific humidity profile - Lower stratosphere (LS)
100 km 500 km 0.5 km 1 km
3h
6h
3h
12 h
5%
10 %
Firm
Signal sizes in section 3.3.7 of
N. Nicholls et al. 1996
Observed Climate Variability
and Change. Chapter 2 of
Climate Change 1995 (IPCC
Second Assessment).
support this need.
GCOS
Specific humidity profile - Lower troposphere (LT)
100 km 500 km 0.1 km 2 km
3h
6h
3h
12 h
5%
10 %
Firm
Signal sizes in section 3.3.7 of
N. Nicholls et al. 1996
Observed Climate Variability
and Change. Chapter 2 of
Climate Change 1995 (IPCC
Second Assessment).
support this need.
GCOS
ANNEX II, p. 7
Application
Requirement
Hor
Res
Min
Vert
Res
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
3h
6h
3h
12 h
1000 kg/m2 2500
Firm
Remarks
Source
Min
Specific humidity profile - Total column
100 km 500 km
GCOS
Wind profile (horizontal component) - Higher stratosphere
& mesosphere (HS & M)
100 km 500 km 2 km
3 km
3h
6h
3h
12 h
3 m/s
7 m/s
Firm
1m/s is typically several % of
climatological wind
strength aloft.
GCOS
Wind profile (horizontal component) - Higher troposphere
100 km 500 km 0.5 km 1 km
3h
6h
3h
12 h
2 m/s
5 m/s
Firm
1m/s is typically several % of
climatological wind strength
aloft.
GCOS
Wind profile (horizontal component) - Lower stratosphere
100 km 500 km 0.5 km 1 km
3h
6h
3h
12 h
2 m/s
5 m/s
Firm
1m/s is typically several % of
climatological wind strength
aloft.
GCOS
Wind profile (horizontal component) - Lower troposphere
100 km 500 km 0.1 km 2 km
3h
6h
3h
12 h
2 m/s
5 m/s
Firm
1m/s is typically several % of
climatological wind strength
aloft.
GCOS
Wind vector over sea surface (horizontal)
100 km 500 km
3h
6h
3h
12 h
2 m/s
5 m/s
Firm
Trend signal shown in Fig.
3.19 of N. Nicholls et al.
1996 Observed Climate
Variability and Change.
Chapter 2 of Climate
Change
1995 (IPCC
Second Assessment)
supports this need.
GCOS
Atmospheric chemistry
Aerosol profile - Higher stratosphere & mesosphere (HS & M
50 km
500 km 1 km
10 km 6 h
24 h
12 h
168 h
10 %
20 %
Firm
Ozone chem, Rad
climatology
WMO
Aerosol profile - Higher troposphere (HT)
50 km
500 km 1 km
5 km
6h
24 h
12 h
168 h
10 %
20 %
Firm
Ozone chem, Rad
climatology
WMO
Aerosol profile - Lower stratosphere (LS)
50 km
500 km 1 km
5 km
6h
24 h
12 h
168 h
10 %
20 %
Firm
Ozone chem, Rad
climatology
WMO
Aerosol profile - Lower troposphere (LT)
50 km
500 km 1 km
5 km
6h
24 h
12 h
168 h
5%
20 %
Firm
Ozone chem, Rad
climatology
WMO
Cloud imagery
Ozone profile - Higher stratosphere & mesosphere (HS &
100 km 200 km
50 km 500 km 1 km
5 km
3h
3h
12 h
48 h
72 h
72 h
72 h
168 h
5%
25 %
Firm
Firm
Rad climatology
Strat chem/clim, Rad
effects/clim, Ozone chem
WMO
WMO
Ozone profile - Higher troposphere (HT)
50 km
500 km 1 km
5 km
3h
168 h
72 h
168 h
3%
20 %
Firm
Trop chem/clim, Rad
effects/clim, Ozone chem
WMO
Ozone profile - Lower stratosphere (LS)
50 km
500 km 1 km
5 km
3h
168 h
72 h
168 h
3%
20 %
Firm
Oxid cap, Rad effects/clim,
Strat climat, O3 chem
WMO
ANNEX II, p. 8
Application
Requirement
Hor
Res
Min
Vert
Res
Obs
Cycle Min
Delay
avail Min
Accuracy
Min
Ozone profile - Lower troposphere (LT)
50 km
500 km 1 km
Ozone profile - Total column
25 km
100 km
Specific humidity profile - Higher stratosphere &
mesosphere (HS & M)
50 km
500 km 1 km
5 km
Specific humidity profile - Higher troposphere (HT)
50 km
500 km 1 km
Specific humidity profile - Lower stratosphere (LS)
50 km
Specific humidity profile - Lower troposphere (LT)
50 km
Confidence
Remarks
Source
5 km
3h
168 h
72 h
168 h
3%
20 %
Firm
Trop chem/clim, Rad
effects/clim, Oxid cap
WMO
6h
48 h
3h
168 h
6 DU
20 DU
Firm
UVB pred/anal, Dynamics
WMO
12 h
72 h
72 h
168 h
5%
20 %
Firm
Atmos dynamics, Rad clim,
Ozone chem, 5% RH
WMO
5 km
12 h
72 h
72 h
168 h
5%
20 %
Firm
Oxid cap, Atm dyncs, Rad
climat, Ozone chem, 5% RH
WMO
500 km 1 km
5 km
12 h
72 h
72 h
168 h
5%
20 %
Firm
Oxid cap, Atm dyncs, Rad
climat, Ozone chem, 5% RH
WMO
500 km 1 km
5 km
6h
72 h
72 h
168 h
5%
20 %
Firm
Oxid cap, Atm dyncs, Rad
clim, Ozone chem, 10% RH
WMO
Trace gas profile BrO - Higher stratosphere & mesosphere
(HS & M)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile BrO - Higher troposphere (HT)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile BrO - Lower stratosphere (LS)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile CFC 11 - Higher stratosphere &
mesosphere (HS & M)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Rad climatology, Atmos
dynamics
WMO
Trace gas profile CFC 11 - Higher troposphere (HT)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Rad climatology
WMO
Trace gas profile CFC 11 - Lower stratosphere (LS)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Atmos dynamics
WMO
Trace gas profile CFC 12 - Higher stratosphere &
mesosphere (HS & M)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Rad climatology, Atmos
dynamics
WMO
Trace gas profile CFC 12 - Higher troposphere (HT)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Rad climatology
WMO
Trace gas profile CFC 12 - Lower stratosphere (LS)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Atmos dynamics
WMO
Trace gas profile CH4 - Higher stratosphere & mesosphere
(HS & M)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
2%
10 %
Firm
Ozone chem
WMO
Trace gas profile CH4 - Higher troposphere (HT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
2%
10 %
Firm
Oxidizing cap, Atm dyncs, 03
chem, Rad clim
WMO
Trace gas profile CH4 - Lower stratosphere (LS)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
2%
10 %
Firm
Oxidizing cap, Atm dyncs, O3
chem, Rad clim
WMO
Trace gas profile CH4 - Lower troposphere (LT)
Trace gas profile ClO - Higher stratosphere & mesosphere
(HS & M)
50 km 500 km 1 km
100 km 500 km 1 km
4 km
3 km
6h
6h
24 h
24 h
72 h
72 h
168 h
168 h
2%
5%
10 %
10 %
Firm
Firm
C budget, bdry
Ozone chem
WMO
WMO
Min
ANNEX II, p. 9
Application
Requirement
Hor
Res
Min
Vert
Res
Obs
Cycle Min
Delay
avail Min
Accuracy
Min
Confidence
Remarks
Source
Min
Trace gas profile ClO - Higher troposphere (HT)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile ClO - Lower stratosphere (LS)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile ClONO2 - Higher stratosphere &
mesosphere (HS & M)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile ClONO2 - Higher troposphere (HT)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile ClONO2 - Lower stratosphere (LS)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile CO - Higher troposphere (HT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap
WMO
Trace gas profile CO - Lower stratosphere (LS)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap
WMO
Trace gas profile CO - Lower troposphere (LT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap
WMO
Trace gas profile CO2 - Lower troposphere (LT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
2%
5%
Firm
C budget, bdry
WMO
Trace gas profile HCl - Higher stratosphere & mesosphere
(HS & M)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
2%
5%
Firm
Ozone chem
WMO
Trace gas profile HCl - Higher troposphere (HT)
100 km 500 km 1 km
1.5 km 6 h
24 h
72 h
168 h
2%
5%
Firm
Ozone chem
WMO
Trace gas profile HCl - Lower stratosphere (LS)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
2%
5%
Firm
Ozone chem
WMO
Trace gas profile HNO3 - Higher stratosphere & mesosphere50 km
(HS & M)
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Atmos dynamics, Ozone chem
WMO
Trace gas profile HNO3 - Higher troposphere (HT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem,
Atm dyncs
WMO
Trace gas profile HNO3 - Lower stratosphere (LS)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem,
Atm dyncs
WMO
Trace gas profile HNO3 - Lower troposphere (LT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem,
Atm dyncs
WMO
Trace gas profile N2O - Higher stratosphere & mesosphere 100 km 500 km 1 km
(HS & M)
3 km
6h
24 h
72 h
168 h
2%
20 %
Firm
Atmos dynamics, Rad clim,
Ozone chem
WMO
Trace gas profile N2O - Higher troposphere (HT)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
2%
20 %
Firm
Rad climatology, Atm dyncs,
Ozone chem
WMO
Trace gas profile N2O - Lower stratosphere (LS)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
2%
20 %
Firm
Rad climatology, Atm dyncs,
Ozone chem
WMO
Trace gas profile NO - Higher stratosphere & mesosphere
(HS & M)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile NO - Higher troposphere (HT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem
WMO
Trace gas profile NO - Lower stratosphere (LS)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem
WMO
ANNEX II, p. 10
Application
Requirement
Trace gas profile NO - Lower troposphere (LT)
Hor
Res
Min
Vert
Res
Obs
Cycle Min
Delay
avail Min
Accuracy
Min
Confidence
Remarks
Source
Min
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem
WMO
Trace gas profile NO2 - Higher stratosphere & mesosphere 50 km
(HS & M)
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Ozone chem
WMO
Trace gas profile NO2 - Higher troposphere (HT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem
WMO
Trace gas profile NO2 - Lower stratosphere (LS)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem
WMO
Trace gas profile NO2 - Lower troposphere (LT)
50 km
500 km 1 km
4 km
6h
24 h
72 h
168 h
5%
10 %
Firm
Oxidizing cap, Ozone chem
WMO
Trace gas profile NO2 - Total column
50 km
500 km
24 h
48 h
72 h
168 h
5%
15 %
Firm
Atmospheric chemistry
WMO
Trace gas profile OH - Higher stratosphere & mesosphere
(HS & M)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
30 %
Firm
Ozone chem
WMO
Trace gas profile OH - Higher troposphere (HT)
100 km 500 km 1 km
1.5 km 6 h
24 h
72 h
168 h
5%
30 %
Firm
Ozone chem
WMO
Trace gas profile OH - Lower stratosphere (LS)
100 km 500 km 1 km
3 km
6h
24 h
72 h
168 h
5%
30 %
Firm
Ozone chem
WMO
Trace gas profile OH - Lower troposphere (LT)
100 km 500 km 1 km
1.5 km 6 h
24 h
72 h
168 h
5%
30 %
Firm
Ozone chem
WMO
CLIVAR
Ocean chlorophyll
Ocean salinity
Ocean suspended sediment concentration
Ocean topography
Ocean yellow substance
Sea surface temperature
Sea-ice cover
Wind vector over sea surface (horizontal)
100 km
100 km
100 km
100 km
100 km
10 km
15 km
50 km
500 km
250 km
500 km
200 km
500 km
50 km
50 km
250 km
1d
30 d
1d
5d
1d
3h
1d
12 h
6d
60 d
6d
10 d
6d
6h
3d
24 h
30 d
9d
30 d
10 d
30 d
24 h
3d
72 h
90 d
120 d
90 d
30 d
90 d
72 h
7d
168 h
Missing
0.1 ‰
Missing
2 cm
Missing
0.1 K
2 % (Max)
1 m/s
Missing
0.3 ‰
Missing
5 cm
Missing
0.3 K
5 % (Max)
5 m/s
Speculative
Reasonable
Speculative
Speculative
Speculative
Reasonable
Reasonable
Reasonable
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
250 km
250 km
250 km 1 km
250 km 1 km
250 km 1 km
250 km 1 km
250 km
250 km
3h
3h
3h
3h
3h
3h
3h
3h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
720 h
720 h
720 h
720 h
720 h
720 h
720 h
720 h
1440 h
1440 h
1440 h
1440 h
1440 h
1440 h
1440 h
1440 h
0.5 km
5 % (Max)
5%
5%
5%
5%
10 g/m2
0.5 K
2 km
20 %
20 %
20 %
20 %
20 %
20 g/m2
2K
Tentative
Reasonable
Tentative
Tentative
Tentative
Tentative
Tentative
Reasonable
Gravity mission needed
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
GEWEX
Cloud base height
Cloud cover
Cloud ice profile - Higher stratosphere & mesosphere (HS &
Cloud ice profile - Higher troposphere (HT)
Cloud ice profile - Lower stratosphere (LS)
Cloud ice profile - Lower troposphere (LT)
Cloud ice profile - Total column
Cloud top temperature
5 km
5 km
5 km
2 km
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
ANNEX II, p. 11
Application
Requirement
Cloud water profile (< 100 µm) - Higher troposphere (HT)
Cloud water profile (< 100 µm) - Lower troposphere (LT)
Cloud water profile (< 100 µm) - Total column
Cloud water profile (> 100 µm) - Higher troposphere (HT)
Cloud water profile (> 100 µm) - Lower troposphere (LT)
Cloud water profile (> 100 µm) - Total column
Land surface temperature
Precipitation index (daily cumulative)
Snow cover 15 km
Snow water equivalent
Soil moisture
Hor
Res
Min
Vert
Res
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
250 km
15 km
15 km
250 km 1 km
250 km 1 km
250 km
250 km 1 km
250 km 1 km
250 km
250 km
250 km
Aerosol profile - Higher troposphere (HT)
Aerosol profile - Lower stratosphere (LS)
Aerosol profile - Lower troposphere (LT)
Atmospheric temperature profile - Higher stratosphere &
mesosphere (HS & M)
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
Remarks
Source
Min
250 km
250 km
10 km 3 h
5 km 3 h
3h
10 km 3 h
5 km 3 h
3h
3h
1h
1d
7d
0.5 d
1d
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
30 d
7d
10 d
720 h
720 h
720 h
720 h
720 h
720 h
720 h
720 h
90 d
30 d
10 d
1440 h 5 %
1440 h 5 %
1440 h 10 g/m2
1440 h 5 %
1440 h 5 %
1440 h 10 g/m2
1440 h 1 K
1440 h 0.5 mm/d
10 % (Max)
90 d
5 mm
30 d
10 g/kg
20 %
20 %
50 g/m2
20 %
20 %
50 g/m2
4K
5 mm/d
50 %
20 mm
50 g/kg
Tentative
Tentative
Tentative
Tentative
Tentative
Tentative
Reasonable
Reasonable
Reasonable
Tentative
Tentative
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
50 km
50 km
50 km
50 km
500 km 1 km
500 km 1 km
500 km 0.1 km
500 km 5 km
5 km
10 km
1 km
10 km
6h
6h
6h
3h
168 h
168 h
168 h
12 h
720 h
720 h
720 h
720 h
1440 h
1440 h
1440 h
1440 h
10 %
10 %
10 %
1K
20 %
20 %
20 %
3K
Tentative
Tentative
Tentative
Reasonable
WCRP
WCRP
WCRP
WCRP
Atmospheric temperature profile - Higher troposphere (HT)
Atmospheric temperature profile - Lower stratosphere (LS)
Atmospheric temperature profile - Lower troposphere (LT)
Downwelling solar radiation at TOA
Sea surface temperature
Sea-ice cover
Significant wave height
Specific humidity profile - Higher stratosphere &
mesosphere (HS & M)
Specific humidity profile - Higher troposphere (HT)
50 km
50 km
50 km
500 km 1 km
3 km
500 km 1 km
3 km
500 km 0.3 km 3 km
50 km
15 km
100 km
50 km
250 km
250 km
250 km
250 km 1 km
3h
3h
3h
1d
1h
1d
12 h
3h
12 h
12 h
12 h
6d
12 h
15 d
24 h
12 h
720 h
720 h
720 h
30 d
720 h
30 d
720 h
720 h
1440 h
1440 h
1440 h
90 d
1440 h
90 d
1440 h
1440 h
0.5 K
0.5 K
0.5 K
0.1 W/m2
0.5 K
5 % (Max)
0.5 m
5%
3K
3K
3K
1 W/m2
2K
50 %
1m
20 %
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
50 km
100 km 0.5 km 2 km
3h
12 h
720 h
1440 h 5 %
20 %
Reasonable
Specific humidity profile - Lower stratosphere (LS)
50 km
250 km 0.5 km 2 km
3h
12 h
720 h
1440 h 5 %
20 %
Reasonable
Global modelling
3 km
Accuracy: Goal 5%, Threshold
10% in RH
Accuracy: Goal 5%, Threshold
10% in RH
Accuracy: Goal 5%, Threshold
10% in RH
WCRP
WCRP
ANNEX II, p. 12
Application
Requirement
Hor
Res
Min
Specific humidity profile - Lower troposphere (LT)
50 km
Wind profile (horizontal component) - Higher stratosphere
& mesosphere (HS & M)
Wind profile (horizontal component) - Higher troposphere
Wind profile (horizontal component) - Lower stratosphere
Wind profile (horizontal component) - Lower troposphere
Wind vector over sea surface (horizontal)
Vert
Res
Obs
Cycle Min
Delay
avail Min
100 km 0.5 km 2 km
3h
12 h
720 h
1440 h 5 %
50 km
500 km 2 km
3h
12 h
720 h
50 km
50 km
50 km
50 km
500 km 1 km
5 km
500 km 1 km
5 km
500 km 0.3 km 5 km
250 km
3h
3h
3h
12 h
12 h
12 h
12 h
24 h
Aerosol profile - Higher troposphere (HT)
Aerosol profile - Lower stratosphere (LS)
Aerosol profile - Lower troposphere (LT)
Aerosol profile - Total column
Air pressure over land surface
Air pressure over sea surface
Air specific humidity (at surface)
Air temperature (at surface)
Atmospheric temperature profile - Higher stratosphere &
mesosphere (HS & M)
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
500 km 1 km
500 km 1 km
500 km 0.1 km
500 km
250 km
250 km
250 km
250 km
500 km 1 km
5 km 6 h
10 km 6 h
1 km 1 h
1h
1h
1h
1h
1h
3 km 1 h
Atmospheric temperature profile - Higher troposphere (HT)
Atmospheric temperature profile - Lower stratosphere (LS)
Atmospheric temperature profile - Lower troposphere (LT)
Cloud base height
Cloud cover
Cloud drop size (at cloud top)
Cloud ice profile - Higher troposphere (HT)
Cloud ice profile - Lower troposphere (LT)
Cloud ice profile - Total column
Cloud imagery
Cloud top height
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
1 km
50 km
500 km 1 km
500 km 1 km
500 km 0.3 km
250 km
250 km
250 km
250 km 1 km
250 km 0.3 km
250 km
50 km
250 km
3 km
3 km
3 km
Min
5 km
Accuracy
Confidence
Remarks
Source
20 %
Reasonable
Accuracy: Goal 5%, Threshold
10% in RH
1440 h 3 m/s
5 m/s
Reasonable
WCRP
720 h
720 h
720 h
720 h
1440 h
1440 h
1440 h
1440 h
2 m/s
2 m/s
2 m/s
1 m/s
5 m/s
5 m/s
5 m/s
5 m/s
Reasonable
Reasonable
Reasonable
Reasonable
WCRP
WCRP
WCRP
WCRP
168 h
168 h
168 h
168 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
1h
1h
1h
1h
1h
1h
1h
168 h
168 h
168 h
168 h
4h
4h
4h
4h
4h
10 %
10 %
10 %
10 %
0.5 hPa
0.5 hPa
5%
0.5 K
0.5 K
20 %
20 %
20 %
20 %
2 hPa
2 hPa
15 %
2K
5K
Tentative
Tentative
Tentative
Tentative
Firm
Firm
Reasonable
Reasonable
Reasonable
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
6h
12 h
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
4h
4h
4h
4h
4h
4h
4h
4h
4h
4h
4h
0.5 K
0.5 K
0.5 K
0.5 km
5 % (Max)
0.5 µm
5%
5%
10 g/m2
3K
3K
3K
1 km
20 %
2 µm
20 %
20 %
20 g/m2
0.5 km
1 km
Firm
Firm
Firm
Tentative
Reasonable
Speculative
Tentative
Tentative
Tentative
Firm
Firm
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
Min
WCRP
Global NWP
1h
1h
1h
1h
1h
1h
10 km 1 h
5 km 1 h
1h
0.5 h
1h
ANNEX II, p. 13
Application
Requirement
Cloud water profile (< 100 µm) - Higher troposphere (HT)
Cloud water profile (< 100 µm) - Lower troposphere (LT)
Cloud water profile (< 100 µm) - Total column
Cloud water profile (> 100 µm) - Higher troposphere (HT)
Cloud water profile (> 100 µm) - Lower troposphere (LT)
Cloud water profile (> 100 µm) - Total column
Dominant wave direction
Dominant wave period
Ice thickness
Land surface temperature
Leaf Area Index (LAI)
Long-wave Earth surface emissivity
Normalized Differential Vegetation Index (NDVI)
Outgoing long-wave radiation at TOA
Outgoing short-wave radiation at TOA
Ozone profile - Higher troposphere (HT)
Ozone profile - Lower stratosphere (LS)
Ozone profile - Lower troposphere (LT)
Ozone profile - Total column
Precipitation index (daily cumulative)
Precipitation rate at the ground (liquid)
Precipitation rate at the ground (solid)
Sea surface temperature
Sea-ice cover
Sea-ice surface temperature
Significant wave height
Snow cover 15 km
Snow water equivalent
Soil moisture
Specific humidity profile - Higher troposphere (HT)
Hor
Res
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
15 km
50 km
50 km
15 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
15 km
15 km
100 km
250 km
15 km
15 km
50 km
Min
Vert
Res
250 km 1 km
250 km 0.3 km
250 km
250 km 1 km
250 km 0.3 km
250 km
250 km
250 km
250 km
250 km
100 km
250 km
100 km
250 km
250 km
500 km 1 km
500 km 1 km
500 km 1 km
100 km
250 km
100 km
100 km
250 km
250 km
200 km
250 km
250 km
250 km
250 km 1 km
Min
Obs
Cycle Min
10 km 1 h
5 km 1 h
1h
10 km 1 h
5 km 1 h
1h
1h
1h
1d
1h
7d
24 h
7d
1h
1h
10 km 1 h
10 km 1 h
5 km 1 h
1h
1h
1h
1h
3h
1d
1h
1h
0.5 d 7 d
0.5 d
1d
3 km 1 h
12 h
12 h
4h
12 h
12 h
12 h
12 h
12 h
7d
12 h
30 d
720 h
30 d
1h
6h
12 h
12 h
12 h
6h
12 h
12 h
12 h
360 h
15 d
7h
12 h
0.5 d
7d
7d
12 h
Delay
avail Min
1h
1h
1h
1h
1h
1h
1h
1h
1d
1h
1d
24 h
1d
240 h
240 h
1h
1h
1h
1h
24 h
1h
1h
3h
1d
1h
1h
1d
0.25 d
0.25 d
1h
Accuracy
4h
5%
4h
5%
4h
10 g/m2
4h
5%
4h
5%
2h
10 g/m2
4h
10 degrees
4h
0.5 s
7d
0.5 m
4h
0.5 K
7d
5 % (Max)
720 h 1 % (Max)
7d
1 % (Max)
720 h 5 W/m2
360 h 5 W/m2
4h
5%
4h
5%
4h
5%
4h
5 DU
720 h 0.5 mm/d
4h
0.1 mm/h
4h
0.1 mm/h
180 h 0.5 K
7d
5 % (Max)
4h
0.5 K
4h
0.5 m
10 % (Max)
1d
5 mm
1d
10 g/kg
4h
5%
Confidence
Remarks
Source
Min
20 %
20 %
50 g/m2
20 %
20 %
50 g/m2
20 degrees
1s
1m
4K
20 %
5 % (Max)
5 % (Max)
10 W/m2
10 W/m2
20 %
20 %
20 %
20 DU
5 mm/d
1 mm/h
1 mm/h
2K
50 %
4K
1m
50 %
20 mm
50 g/kg
20 %
Tentative
Tentative
Tentative
Tentative
Tentative
Tentative
Firm
Firm
Speculative
Firm
Tentative
Tentative
Tentative
Firm
Firm
Tentative
Tentative
Tentative
Reasonable
Reasonable
Tentative
Tentative
Firm
Firm
Reasonable
Firm
Reasonable
Tentative
Reasonable
Firm
Accuracy 5% in RH
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
ANNEX II, p. 14
Application
Requirement
Specific humidity profile - Lower troposphere (LT)
Specific humidity profile - Total column
Wind profile (horizontal component) - Higher troposphere
Wind profile (horizontal component) - Lower stratosphere
Wind profile (horizontal component) - Lower troposphere
Wind profile (vertical component) - Higher troposphere
Wind profile (vertical component) - Lower stratosphere
Wind profile (vertical component) - Lower troposphere
Wind speed over land surface (horizontal)
Wind speed over sea surface (horizontal)
Wind vector over land surface (horizontal)
Wind vector over sea surface (horizontal)
Hor
Res
Min
Vert
Res
Obs
Cycle Min
Delay
avail Min
Accuracy
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
250 km 0.4 km
500 km
500 km 1 km
500 km 1 km
500 km 0.4 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
250 km
250 km
250 km
250 km
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
4h
4h
4h
4h
4h
4h
4h
4h
4h
4h
4h
4h
5%
1 kg/m2
1 m/s
1 m/s
1 m/s
1 cm/s
1 cm/s
1 cm/s
0.5 m/s
0.5 m/s
0.5 m/s
0.5 m/s
25 km
200 km
100 km
10 km
10 km
25 km
25 km
100 km
500 km
300 km
300 km
100 km
100 km
100 km
1d
10 d
10 d
6h
1d
24 h
24 h
3d
30 d
30 d
720 h
6d
168 h
168 h
1d
10 d
10 d
6h
0.125
24 h
24 h
3d
30 d
30 d
720 h
1d
168 h
168 h
25 km
100 km
7d
30 d
2d
15 d
1 km
1 km
250000
10 m
0.01
100 m
50 km
50 km
12 d
12 d
1d
365 d
168 h
50 y
1d
1d
7d
1d
24 h
30 d
4d
10 % (Max)
4d
1m
50 classes
7d
168 h 0.3 K
600 d 1 m (vert.)
Min
2 km
10 km
10 km
5 km
10 km
10 km
5 km
Confidence
Remarks
Source
20 %
5 kg/m2
8 m/s
5 m/s
5 m/s
5 cm/s
5 cm/s
5 cm/s
3 m/s
3 m/s
5 m/s
5 m/s
Firm
Firm
Firm
Firm
Firm
Speculative
Speculative
Speculative
Reasonable
Firm
Reasonable
Firm
Accuracy 5% in RH
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
0.1 mg/m3
0.1 ‰
2 cm
0.1 K
2 % (Max)
1 m/s
1 m/s
0.5 mg/m3
1‰
5 cm
1K
10 %
2 m/s
2 m/s
Firm
Firm
Firm
Firm
Firm
Firm
Firm
GOOS
GOOS
GOOS
GOOS
GOOS
GOOS
GOOS
2 cm
10 cm
Firm
GOOS
20 %
2m
5 classes
Firm
Firm
Reasonable
Reasonable
Reasonable
Reasonable
WMO
WMO
WMO
WMO
WMO
WMO
Min
GOOS Climate - large scale
Ocean chlorophyll
Ocean salinity
Ocean topography
Sea surface temperature
Sea-ice cover
Wind speed over sea surface (horizontal)
Wind vector over sea surface (horizontal)
GOOS Climate - mesoscale
Ocean topography
Hydrology
Iceberg fractional cover
Iceberg height
Land cover 10 m
Land surface imagery
Land surface temperature
Land surface topography
250000
250 km
1000 m
1d
1d
0.02 y 1 y
1d
1h
10 y
3K
5 m (vert.)
ANNEX II, p. 15
Application
Requirement
Leaf Area Index (LAI)
Long-wave Earth surface emissivity
Normalized Differential Vegetation Index (NDVI)
Outgoing long-wave radiation at TOA
Outgoing short-wave radiation at TOA
Permafrost 0.1 km
Sea level 0.1 km
Snow cover 0.1 km
Snow melting conditions
Snow water equivalent
Soil moisture
Vegetation type
Hor
Res
Min
Vert
Res
Min
0.01
0.01
0.01
10 km
0.1 km
100 km
10 km
100 km
0.1 km
0.1 km
0.01
10 m
10 km
250 km
250 km
100 km
200 km
5 km
5 km
5 km
5 km
5 km
1 km
1 km
1 km
1 km
10 km
250 km
5 km
5 km
10 km
1 km
5 km
50 km
20 km
50 km
200 km 1 km
3 km
200 km 0.5 km 1 km
20 km
5 km
10 km
10 km
0.01 h
0.25 d
250 km
50 km
200 km
50 km
10 km
10 km
10 km
250 km
1000 m
Obs
Cycle Min
7d
24 h
1d
1h
1h
0.25 d 3 d
1d
7d
1d
7d
0.5 h
1d
1d
7d
Delay
avail Min
Accuracy
Confidence
Remarks
Source
Min
24 d
288 h
30 d
12 h
6h
0.25 d
1d
1d
12 h
7d
3d
365 d
1d
24 h
1d
24 h
24 h
6d
7d
6d
1h
1d
1d
1d
5d
5 % (Max)
288 h 5 % (Max)
7d
1 % (Max)
168 h 5 W/m2
168 h 5 W/m2
5 % (Max)
2 cm 10 cm
5 % (Max)
144 h 5 classes
6d
5 mm
144 d 10 g/kg
30 d
50 classes
20 %
20 %
20 %
20 W/m2
20 W/m2
25 %
Reasonable
20 %
2 classes
20 mm
50 g/kg
5 classes
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
WMO
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
WMO
WMO
WMO
WMO
WMO
WMO
12 h
1h
0.5 h
1h
1h
1h
0.5 h
0.5 h
0.5 h
0.02 h
1d
12 d
1h
6h
1h
12 d
0.25 h
0.25 h
0.25 h
0.08 h
0.08 h
0.016
0.25 h
0.02 h
0.02 h
0.5 h
4d
1d
0.08 h
0.5 h
0.08 h
1d
2h
10 %
0.5 h 0.5 K
1h
Missing
0.5 h 1 K
0.5 h 0.5 K
0.5 h 5 % (Max)
1h
0.5 h 0.1 km
0.5 h 0.5 K
10 classes
10 % 20 %
4d
500 K
0.5 h 50 m
2h
0.1 km
0.5 h 0.5 K
5d
5 % (Max)
20 %
1K
Missing
2K
2K
20 %
Firm
Reasonable
Firm
Firm
Firm
Firm
Firm
Firm
Firm
Firm
WMO
Firm
Firm
Firm
Firm
Firm
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
Nowcasting
Aerosol profile - Total column
Air temperature (at surface)
Atmospheric stability index
Atmospheric temperature profile - Higher troposphere (HT)
Atmospheric temperature profile - Lower troposphere (LT)
Cloud cover
Cloud imagery
Cloud top height
Cloud top temperature
Cloud type 1 km
Fire area 5 km
Fire temperature
Height of the top of the Planetary Boundary Layer
Height of tropopause
Land surface temperature
Normalized Differential Vegetation Index (NDVI)
0.25 h
0.25 h
0.08 h
0.25 h
0.25 h
0.0083
0.05 h
0.01 h
0.01 h
0.5 h
12 d
0.25 d
0.25 h
0.5 h
0.25 h
1d
1 km
2K
5 classes
Firm
1000 K
500 m
1 km
3K
10 %
WMO
WMO
WMO
WMO
WMO
ANNEX II, p. 16
Application
Requirement
Ocean currents (vector)
Precipitation rate at the ground (liquid)
Precipitation rate at the ground (solid)
Sea surface temperature
Sea-ice cover
Snow cover 5 km
Soil moisture
Specific humidity profile - Higher troposphere (HT)
Specific humidity profile - Lower troposphere (LT)
Specific humidity profile - Total column
Temperature of tropopause
Wind profile (horizontal component) - Higher troposphere
Wind profile (horizontal component) - Lower stratosphere
Wind profile (horizontal component) - Lower troposphere
Wind profile (vertical component) - Lower troposphere
Wind speed over land surface (horizontal)
Wind speed over sea surface (horizontal)
Wind vector over land surface (horizontal)
Wind vector over sea surface (horizontal)
Hor
Res
Min
Vert
Res
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
Remarks
Source
Min
10 km
5 km
5 km
5 km
5 km
50 km
5 km
5 km
5 km
5 km
10 km
5 km
5 km
5 km
5 km
5 km
5 km
5 km
5 km
50 km
50 km
50 km
50 km
50 km
0.25 d
0.08 h
0.25 h
1h
1d
0.04 d 0.25 d
50 km
0.5 d
200 km 1 km
3 km 0.25 h
200 km 0.5 km 1 km 0.25 h
50 km
0.25 h
200 km
0.5 h
200 km 0.5 km 1 km 0.25 h
200 km 0.5 km 1 km 0.25 h
200 km 0.5 km 1 km 0.25 h
200 km 0.5 km 2 km 0.25 h
50 km
0.25 h
50 km
0.25 h
50 km
0.25 h
50 km
0.25 h
6d
1h
1h
6h
24 d
0.04 d
2d
1h
1h
1h
6h
4h
6h
6h
1h
3h
3h
3h
3h
0.25 d
0.08 h
0.5 h
1h
1d
0.25 d
0.25 d
0.08 h
0.08 h
0.08 h
0.5 h
0.08 h
0.25 h
0.25 h
0.08 h
0.25 h
0.25 h
0.25 h
0.25 h
4d
0.5 cm/s
0.5 h 0.1 mm/h
0.5 h 0.1 mm/h
2h
0.5 K
6d
10 % (Max)
10 % (Max)
1d
10 g/kg
0.5 h 5 %
0.5 h 5 %
0.5 h 1 kg/m2
2h
0.5 K
0.5 h 1 m/s
2h
1 m/s
2h
1 m/s
0.5 h 1 cm/s
1h
1 m/s
1h
1 m/s
1h
1 m/s
1h
1 m/s
1 cm/s
1 mm/h
1 mm/h
2K
20 %
20 %
50 g/kg
20 %
20 %
5 kg/m2
2K
8 m/s
5 m/s
5 m/s
5 cm/s
5 m/s
5 m/s
5 m/s
5 m/s
Firm
Firm
Firm
Firm
Firm
Firm
Reasonable
Firm
Firm
Firm
Firm
Firm
Firm
Firm
Firm
Firm
Firm
Firm
Firm
100 km
200 km
30 km
100 km
250 km
500 km
100 km
500 km
10 d
24 h
1d
12 h
30 d
72 h
7d
24 h
0.125
3h
0.125
3h
1d
12 h
1d
12 h
5 cm
0.5 K
2 % (Max)
2 m/s
10 cm
2K
5 % (Max)
5 m/s
Firm
Firm
Firm
Firm
GCOS
GCOS
GCOS
GCOS
10 km
10 km
10 km
10 km
250 km
250 km
250 km
250 km
0.5 h
0.5 h
0.5 h
0.5 h
12 h
12 h
12 h
12 h
0.5 h
0.5 h
0.5 h
0.5 h
2h
2h
2h
2h
0.5 hPa
0.5 hPa
5%
0.5 K
1 hPa
1 hPa
15 %
2K
Firm
Firm
Reasonable
Reasonable
WMO
WMO
WMO
WMO
Accuracy 10% in RH
Accuracy 5% in RH
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
OOPC
Ocean topography
Sea surface temperature
Sea-ice cover
Wind vector over sea surface (horizontal)
Regional NWP
Air pressure over land surface
Air pressure over sea surface
Air specific humidity (at surface)
Air temperature (at surface)
ANNEX II, p. 17
Application
Requirement
Atmospheric temperature profile - Higher troposphere (HT)
Atmospheric temperature profile - Lower stratosphere (LS)
Atmospheric temperature profile - Lower troposphere (LT)
Cloud base height
Cloud cover
Cloud drop size (at cloud top)
Cloud ice profile - Higher troposphere (HT)
Cloud ice profile - Lower troposphere (LT)
Cloud ice profile - Total column
Cloud imagery
Cloud top height
Cloud water profile (< 100 µm) - Higher troposphere (HT)
Cloud water profile (< 100 µm) - Lower troposphere (LT)
Cloud water profile (< 100 µm) - Total column
Cloud water profile (> 100 µm) - Higher troposphere (HT)
Cloud water profile (> 100 µm) - Lower troposphere (LT)
Cloud water profile (> 100 µm) - Total column
Dominant wave direction
Dominant wave period
Ice thickness
Land surface temperature
Leaf Area Index (LAI)
Long-wave Earth surface emissivity
Normalized Differential Vegetation Index (NDVI)
Outgoing long-wave radiation at TOA
Outgoing short-wave radiation at TOA
Ozone profile - Higher troposphere (HT)
Ozone profile - Lower stratosphere (LS)
Ozone profile - Lower troposphere (LT)
Ozone profile - Total column
Hor
Res
Min
Vert
Res
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
1 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
5 km
10 km
10 km
5 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
500 km 1 km
500 km 1 km
500 km 0.3 km
250 km
250 km
250 km
250 km 1 km
250 km 0.3 km
250 km
50 km
250 km
250 km 1 km
250 km 0.3 km
250 km
250 km 1 km
250 km 0.3 km
250 km
50 km
50 km
250 km
250 km
50 km
250 km
50 km
250 km
250 km
200 km 1 km
200 km 1 km
200 km 1 km
100 km
Min
3 km
3 km
3 km
10 km
5 km
10 km
5 km
10 km
5 km
10 km
10 km
5 km
Obs
Cycle Min
Delay
avail Min
Accuracy
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.25 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
1h
1h
1d
0.5 h
7d
24 h
7d
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
1d
0.5 h
1d
24 h
1d
240 h
240 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 K
0.5 K
0.5 K
0.5 km
5 % (Max)
0.5 µm
5%
5%
10 g/m2
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
6h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
7d
12 h
30 d
720 h
30 d
1h
1h
3h
3h
3h
6h
2h
2h
2h
3h
2h
2h
2h
2h
2h
2h
2h
2h
2h
2h
2h
2h
4h
2h
2h
7d
2h
7d
720 h
7d
720 h
360 h
2h
2h
2h
2h
Confidence
Remarks
Source
Min
0.5 km
5%
5%
10 g/m2
5%
5%
10 g/m2
10 degrees
0.5 s
0.5 m
0.5 K
5 % (Max)
1 % (Max)
1 % (Max)
5 W/m2
5 W/m2
5%
5%
5%
5 DU
3K
3K
3K
1 km
20 %
2 µm
20 %
20 %
20 g/m2
Firm
Firm
Firm
Tentative
Reasonable
Firm
Tentative
Tentative
Tentative
Firm
1 km
Firm
20 %
Tentative
20 %
Tentative
50 g/m2
Tentative
20 %
Tentative
20 %
Tentative
50 g/m2
Tentative
20 degrees Firm
1s
Firm
1m
Speculative
4K
Firm
20 %
Tentative
5 % (Max) Tentative
5 % (Max) Tentative
10 W/m2
Firm
10 W/m2
Firm
20 %
Tentative
20 %
Tentative
20 %
Tentative
20 DU
Reasonable
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
ANNEX II, p. 18
Application
Requirement
Precipitation index (daily cumulative)
Precipitation rate at the ground (liquid)
Precipitation rate at the ground (solid)
Sea surface temperature
Sea-ice cover
Sea-ice surface temperature
Significant wave height
Snow cover 5 km
Snow water equivalent
Soil moisture
Specific humidity profile - Higher troposphere (HT)
Specific humidity profile - Lower troposphere (LT)
Specific humidity profile - Total column
Wind profile (horizontal component) - Higher troposphere
Wind profile (horizontal component) - Lower stratosphere
Wind profile (horizontal component) - Lower troposphere
Wind profile (vertical component) - Higher troposphere
Wind profile (vertical component) - Lower stratosphere
Wind profile (vertical component) - Lower troposphere
Wind speed over land surface (horizontal)
Wind speed over sea surface (horizontal)
Wind vector over land surface (horizontal)
Wind vector over sea surface (horizontal)
Hor
Res
Min
10 km
10 km
10 km
25 km
25 km
5 km
10 km
250 km
5 km
5 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
10 km
250 km
50 km
100 km
50 km
50 km
100 km
50 km
50 km
500 km
25 km
100 km
100 km
500 km
Vert
Res
Min
0.5 d
250 km
250 km
100 km 1 km
100 km 0.4 km
250 km
500 km 1 km
500 km 1 km
500 km 0.4 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
250 km
100 km
250 km
100 km
3 km
2 km
10 km
10 km
5 km
10 km
10 km
5 km
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
0.5 h
0.5 h
0.5 h
1h
0.5 d
0.5 h
1h
7d
0.25 d
1d
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
12 h
6h
12 h
12 h
7d
12 h
12 h
0.25 d
7d
7d
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
24 h
0.5 h
0.5 h
1h
0.3 d
0.5 h
1h
1d
0.25 d
7d
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
0.5 h
720 h 0.5 mm/d
2h
0.1 mm/h
2h
0.1 mm/h
24 h
0.5 K
3d
5 % (Max)
2h
0.5 K
2h
0.1 m
10 % (Max)
1d
5 mm
7d
10 g/kg
2h
5%
2h
5%
2h
1 kg/m2
2h
1 m/s
2h
1 m/s
2h
1 m/s
2h
1 cm/s
2h
1 cm/s
2h
1 cm/s
2h
0.5 m/s
2h
0.5 m/s
2h
0.5 m/s
2h
0.5 m/s
5 mm/d
1 mm/h
1 mm/h
1K
50 %
4K
0.2 m
50 %
20 mm
50 g/kg
20 %
20 %
5 kg/m2
8 m/s
5 m/s
5 m/s
5 cm/s
5 cm/s
5 cm/s
3 m/s
3 m/s
5 m/s
5 m/s
Reasonable
Tentative
Tentative
Firm
Firm
Firm
Firm
Reasonable
Tentative
Reasonable
Firm
Firm
Firm
Firm
Firm
Firm
Speculative
Speculative
Speculative
Reasonable
Firm
Reasonable
Firm
7d
30 y
1d
30 d
1d
30 d
12 y
3d
60 d
6d
1d
24 y
1d
9d
30 d
30 d
1 cm
3d
120 d
90 d
10 %
Firm
0.5 mg/m3
0.3 ‰
Missing
Firm
WMO
Firm
Reasonable
Speculative
Remarks
Source
Min
Accuracy 5% in RH
Accuracy 5% in RH
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
S&IA
Fractional Photosynthetically Active Radiation (FPAR)
Geoid
100 km
Ocean chlorophyll
Ocean salinity
Ocean suspended sediment concentration
20 y
100 km
250 km
500 km
5 % (Max)
5 cm
0.1 mg/m3
0.1 ‰
Missing
WMO
WMO
WMO
WMO
ANNEX II, p. 19
Application
Requirement
Hor
Res
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
25 km
100 km
50 km
50 km
50 km
50000
100 km
500 km
250 km
500 km
500 km
500000
7d
1d
3h
1d
1d
7d
30 d
6d
12 h
7d
7d
30 d
2d
30 d
3h
1d
1d
1d
15 d
90 d
24 h
7d
7d
7d
1 cm
Missing
0.1 K
5 mm
10 g/kg
18 classes
4 cm
Missing
0.5 K
20 mm
50 g/kg
9 classes
Firm
Speculative
Firm
Tentative
Reasonable
Firm
Aerosol profile - Higher stratosphere & mesosphere (HS &
Aerosol profile - Higher troposphere (HT)
Aerosol profile - Lower stratosphere (LS)
Aerosol profile - Lower troposphere (LT)
Atmospheric temperature profile - Higher stratosphere &
mesosphere (HS & M)
100 km
100 km
100 km
100 km
50 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
2 km
2 km
2 km
2 km
2 km
6h
6h
6h
6h
6h
72 h
72 h
72 h
72 h
72 h
24 h
24 h
24 h
24 h
24 h
168 h
168 h
168 h
168 h
168 h
10 %
10 %
10 %
10 %
0.5 K
20 %
20 %
20 %
20 %
1K
Tentative
Tentative
Tentative
Tentative
Reasonable
WCRP
WCRP
WCRP
WCRP
WCRP
Atmospheric temperature profile - Higher troposphere (HT)
Atmospheric temperature profile - Lower stratosphere (LS)
Atmospheric temperature profile - Lower troposphere (LT)
Outgoing long-wave radiation at TOA
Outgoing short-wave radiation at TOA
Ozone profile - Higher stratosphere & mesosphere (HS &
Ozone profile - Higher troposphere (HT)
Ozone profile - Lower stratosphere (LS)
Ozone profile - Lower troposphere (LT)
Specific humidity profile - Higher stratosphere &
mesosphere (HS & M)
Specific humidity profile - Higher troposphere (HT)
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
50 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
250 km
250 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
500 km 0.5 km
2 km
2 km
2 km
2 km
2 km
2 km
2 km
2 km
6h
6h
6h
3h
3h
6h
6h
6h
6h
6h
72 h
72 h
72 h
6h
6h
72 h
72 h
72 h
72 h
72 h
24 h
24 h
24 h
720 h
720 h
24 h
24 h
24 h
24 h
24 h
168 h
168 h
168 h
2160 h
2160 h
168 h
168 h
168 h
168 h
168 h
0.5 K
0.5 K
0.5 K
5 W/m2
5 W/m2
5%
5%
5%
5%
2%
1K
1K
1K
10 W/m2
10 W/m2
10 %
10 %
10 %
10 %
5%
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
Reasonable
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
WCRP
50 km
500 km 0.5 km 2 km
6h
72 h
24 h
168 h
2%
5%
Reasonable
Specific humidity profile - Lower stratosphere (LS)
50 km
500 km 0.5 km 2 km
6h
72 h
24 h
168 h
2%
5%
Reasonable
Specific humidity profile - Lower troposphere (LT)
50 km
500 km 0.5 km 2 km
6h
72 h
24 h
168 h
2%
5%
Reasonable
Ocean topography
Ocean yellow substance
Sea surface temperature
Snow water equivalent
Soil moisture
Vegetation type
Vert
Res
Min
Confidence
Remarks
Source
Min
Tropical ocean most
WMO
WMO
WMO
WMO
WMO
WMO
SPARC
Accuracy: Goal 1%, Threshold
5% in RH
Accuracy: Goal 1%, Threshold
5% in RH
Accuracy: Goal 1%, Threshold
5% in RH
Accuracy: Goal 1%, Threshold
5% in RH
WCRP
WCRP
WCRP
ANNEX II, p. 20
Application
Requirement
Hor
Res
Min
Vert
Res
Min
Obs
Cycle Min
Delay
avail Min
Accuracy
Confidence
Remarks
Source
Min
Wind profile (horizontal component) - Higher stratosphere
& mesosphere (HS & M)
200 km 500 km 0.5 km 2 km
6h
72 h
24 h
168 h
3 m/s
5 m/s
Reasonable
WCRP
Wind profile (horizontal component) - Higher troposphere
Wind profile (horizontal component) - Lower stratosphere
Wind profile (horizontal component) - Lower troposphere
200 km 500 km 0.5 km 2 km
200 km 500 km 0.5 km 2 km
200 km 500 km 0.5 km 2 km
6h
6h
6h
72 h
72 h
72 h
24 h
24 h
24 h
168 h
168 h
168 h
3 m/s
3 m/s
3 m/s
5 m/s
5 m/s
5 m/s
Reasonable
Reasonable
Reasonable
WCRP
WCRP
WCRP
10 km
20 km
20 km
20 km
20 km
1 km
1 km
200 km
50 km
50 km
20 km
20 km
20 km
5 km
20 km
20 km
20 km
20 km
20 km
20 km
20 km
20 km
20 km
1h
1h
3h
3h
3h
0.25 h
0.25 h
6h
3h
3h
0.25 h
1h
3h
3h
3h
3h
3h
3h
3h
1h
1h
1h
1h
12 h
6h
12 h
12 h
12 h
6h
6h
0.25 h
12 h
12 h
12 h
6h
6h
24 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
12 h
1h
1h
1h
1h
1h
0.25 h
0.25 h
6h
1h
1h
0.25 h
0.25 h
0.25 h
1h
1h
1h
1h
1h
1h
1h
1h
1h
1h
4h
0.5 K
3h
Missing
3h
0.5 K
3h
0.5 K
3h
0.5 K
6h
6h
0.5 km
10 classes
3h
20 degrees
3h
0.5 s
6h
5 DU
6h
0.1 mm/h
6h
0.1 mm/h
24 h
0.5 K
3h
5%
3h
5%
3h
2 m/s
3h
2 m/s
3h
2 m/s
3h
2 m/s
3h
2 m/s
3h
2 m/s
3h
2 m/s
Synoptic Meteorology
Air temperature (at surface)
Atmospheric stability index
Atmospheric temperature profile - Higher troposphere (HT)
Atmospheric temperature profile - Lower stratosphere (LS)
Atmospheric temperature profile - Lower troposphere (LT)
Cloud imagery
Cloud top height
Cloud type 20 km
Dominant wave direction
Dominant wave period
Ozone profile - Total column
Precipitation rate at the ground (liquid)
Precipitation rate at the ground (solid)
Sea surface temperature
Specific humidity profile - Higher troposphere (HT)
Specific humidity profile - Lower troposphere (LT)
Wind profile (horizontal component) - Higher troposphere
Wind profile (horizontal component) - Lower stratosphere
Wind profile (horizontal component) - Lower troposphere
Wind speed over land surface (horizontal)
Wind speed over sea surface (horizontal)
Wind vector over land surface (horizontal)
Wind vector over sea surface (horizontal)
100 km
200 km
200 km 0.1 km 2 km
200 km 0.1 km 2 km
200 km 0.1 km 2 km
10 km
10 km
0.25 h
200 km
200 km
50 km
100 km
100 km
50 km
200 km 0.1 km 2 km
200 km 0.1 km 2 km
200 km 0.1 km 2 km
200 km 0.1 km 2 km
200 km 0.1 km 2 km
200 km
200 km
200 km
200 km
2K
Missing
3K
3K
3K
Firm
Firm
Firm
Firm
Firm
Firm
2 km
Firm
5 classes Firm
30 degrees Firm
1s
Firm
20 DU
Firm
1 mm/h
Firm
1 mm/h
Firm
2K
Firm
20 %
Firm
20 %
Firm
8 m/s
Firm
5 m/s
Firm
5 m/s
Firm
5 m/s
Firm
5 m/s
Firm
5 m/s
Firm
5 m/s
Firm
Accuracy 10% in RH
Accuracy 10% in RH
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
WMO
ANNEX II, p. 21
Application
Requirement
Hor
Res
Min
Aerosol profile - Total column
Aerosol profile - Total column
Air specific humidity (at surface)
1 km
1 km
25 km
Air specific humidity (at surface)
Vert
Res
Obs
Cycle Min
Delay
avail Min
Accuracy
4 km
4 km
100 km
24 h
24 h
3h
48 h
48 h
6h
24 h
24 h
24 h
120 h
120 h
72 h
Missing
Missing
1%
Missing
Missing
2%
Speculative
Speculative
Speculative
25 km
100 km
3h
6h
24 h
72 h
1%
2%
Speculative
Air temperature (at surface)
Air temperature (at surface)
Cloud imagery
Cloud imagery
Downwelling long-wave radiation at the Earth surface
25 km
25 km
1 km
1 km
25 km
100 km
100 km
10 km
10 km
100 km
3h
3h
3h
3h
3h
12 h
12 h
12 h
12 h
6h
24 h
24 h
12 h
12 h
24 h
48 h
48 h
24 h
24 h
120 h
0.2 K
0.2 K
0.5 K
0.5 K
5 W/m2
10 W/m2
Speculative
Speculative
Speculative
Speculative
Speculative
Temperature - air
Temperature - air
Cloud cover
Cloud cover
Radiation - outgoing
long-wave in situ
GCOS
GTOS
GTOS
GCOS
GTOS
Downwelling long-wave radiation at the Earth surface
25 km
100 km
3h
6h
24 h
120 h
5 W/m2
10 W/m2
Speculative
Radiation - outgoing
long-wave in situ
GCOS
Downwelling short-wave radiation at the Earth surface
25 km
100 km
24 h
120 h
24 h
720 h
5 W/m2
10 W/m2
Speculative
Radiation - reflected short
wave in situ
GCOS
Downwelling short-wave radiation at the Earth surface
25 km
100 km
24 h
120 h
24 h
720 h
5 W/m2
10 W/m2
Speculative
Radiation - reflected short
wave in situ
GTOS
Fire area 0.1 km
Fire area 0.1 km
Fire temperature
Fire temperature
Fractional Photosynthetically Active Radiation (FPAR)
Fractional Photosynthetically Active Radiation (FPAR)
Glacier cover
Glacier cover
Ice-sheet topography
Ice-sheet topography
Land cover 100 m
Land cover 100 m
1 km
1 km
0.1 km
0.1 km
0.1 km
0.1 km
10 m
10 m
0.01
0.01
1000 m
1000 m
365 d
365 d
10 d
10 d
10 d
10 d
30 y
30 y
5y
5y
10 y
10 y
30 d
30 d
365 d
365 d
30 d
30 d
50 y
50 y
10 y
10 y
90 d
90 d
90 d
90 d
30 d
30 d
10 d
10 d
720 d
720 d
365 d
365 d
365 d
365 d
10 % 20 %
10 % 20 %
90 d
50 K
90 d
50 K
30 d
5 % (Max)
30 d
5 % (Max)
1500 d 10 % (Max)
1500 d 10 % (Max)
720 d 50 cm (vert.)
720 d 50 cm (vert.)
50 classes
50 classes
Tentative
Tentative
200 K
200 K
10 %
10 %
20 %
20 %
100 cm
100 cm
20 classes
20 classes
GCOS
GTOS
Tentative
Tentative
Tentative
Tentative
Speculative
Speculative
Speculative
Speculative
Tentative
Tentative
Min
Confidence
Remarks
Source
Min
Terrestrial Climate
10 d
10 d
1 km
1 km
2 km
2 km
100 m
100 m
0.05
0.05
1y
1y
Atmospheric water content
near the surface
Atmospheric water content
near the surface
Glacier inventory
Glacier inventory
Ice sheet geometry
Ice sheet geometry
GTOS
GCOS
GTOS
GCOS
GCOS
GTOS
GTOS
GCOS
GCOS
GTOS
GTOS
GCOS
GCOS
GTOS
ANNEX II, p. 22
Application
Requirement
Hor
Res
Min
Land surface imagery
Land surface imagery
Land surface topography
Land surface topography
Leaf Area Index (LAI)
Leaf Area Index (LAI)
Outgoing long-wave Earth surface
1m
1m
10 m
10 m
0.1 km
0.1 km
25 km
Outgoing long-wave Earth surface
Vert
Res
Obs
Cycle Min
Delay
avail Min
10 m
10 m
1000 m
1000 m
1 km
1 km
100 km
1500 d
1500 d
10 y
10 y
10 d
10 d
3h
3000 d
3000 d
30 y
30 y
30 d
30 d
6h
1500 d
1500 d
720 d
720 d
10 d
10 d
24 h
3000 d
3000 d
1500 d
1500 d
30 d
30 d
120 h
30 m (vert.)
30 m (vert.)
20 % (Max)
20 % (Max)
5 W/m2
25 km
100 km
3h
6h
24 h
120 h
Outgoing long-wave radiation at TOA
50 km
100 km
480 h
Outgoing long-wave radiation at TOA
50 km
100 km
Ozone profile - Total column
Ozone profile - Total column
Permafrost 0.01
Permafrost 0.01
Precipitation rate at the ground (liquid)
Precipitation rate at the ground (liquid)
Precipitation rate at the ground (solid)
Precipitation rate at the ground (solid)
Snow cover 1 km
Snow cover 1 km
Snow melting conditions
Snow melting conditions
Snow water equivalent
Snow water equivalent
Soil moisture
Soil moisture
Wind vector over land surface (horizontal)
Wind vector over land surface (horizontal)
1 km
1 km
1 km
1 km
1 km
1 km
1 km
1 km
5 km
5 km
10 km
10 km
10 km
10 km
25 km
25 km
25 km
25 km
8 km
8 km
Min
10 d
10 d
10 km
10 km
10 km
10 km
1d
1d
25 km
25 km
25 km
25 km
100 km
100 km
100 km
100 km
Confidence
Remarks
Source
100 m
100 m
100 %
100 %
10 W/m2
Speculative
Speculative
Firm
Speculative
Tentative
Tentative
Speculative
Glacier length
Glacier length
Topography
Topography
GCOS
GTOS
GCOS
GTOS
GCOS
GTOS
GTOS
5 W/m2
10 W/m2
Speculative
Radiation - outgoing
long-wave in situ
GCOS
1440 h 816 h
2448 h 5 W/m2
10 W/m2
Speculative
Radiation - outgoing long
wave satellite
GCOS
480 h
1440 h 30 h
90 h
10 W/m2
Speculative
Radiation - outgoing long
wave satellite
GTOS
24 h
24 h
365 d
365 d
3h
3h
3h
3h
3d
3d
24 h
24 h
1d
1d
1d
1d
24 h
24 h
48 h
48 h
90 d
90 d
6h
6h
6h
6h
2d
2d
72 h
72 h
3d
3d
5d
5d
120 h
120 h
720 h Missing
720 h Missing
Missing Missing
Missing Missing
120 h 0.05 mm/h
120 h 0.05 mm/h
120 h 0.05 mm/h
120 h 0.05 mm/h
5 % (Max)
5 % (Max)
72 h
6 classes
72 h
6 classes
3d
5 mm
3d
5 mm
5d
Missing
5d
Missing
240 h 2 m/s
240 h 2 m/s
Missing
Missing
Speculative
Speculative
0.1 mm/h
0.1 mm/h
0.1 mm/h
0.1 mm/h
10 %
10 %
2 classes
2 classes
10 mm
10 mm
Missing
Missing
5 m/s
5 m/s
Speculative
Speculative
Permafrost extent
Permafrost extent
Tentative
Tentative
Tentative
Tentative
Tentative
Snow cover area
Tentative
Snow cover area
Tentative
Snow surface state
Tentative
Snow surface state
Speculative
Speculative
Speculative
Speculative
Speculative
Wind velocity
Speculative
Wind velocity
240 h
240 h
365 d
365 d
24 h
24 h
24 h
24 h
3d
3d
48 h
48 h
2d
2d
3d
3d
24 h
24 h
Accuracy
Min
5 W/m2
Radiation - outgoing
long-wave in situ
GCOS
GTOS
GTOS
GCOS
GTOS
GCOS
GTOS
GCOS
GCOS
GTOS
GCOS
GTOS
GTOS
GCOS
GCOS
GTOS
GTOS
GCOS
ANNEX III
PROCESSING, INTERPRETATION, DISSEMINATION AND STORAGE OF SATELLITE DATA
Since the first environmental satellite launch, satellite data, information and products have
played a steadily increasing role in the successful fulfilment of many WMO programmes, ranging
from operational meteorology to climate research. Chapters 5 and 8 describe the continuing role
of satellites in the monitoring of the atmosphere and ocean, the cryosphere and land surfaces and
the use of the observed data to improve our understanding of the complex interaction of
meteorological and climatological processes. There is no possibility of advancing scientific
research or improving operational data assimilation systems without the availability of retrospective
observed data and products. The long-term stewardship of this information is only possible
through planned, carefully constructed systems of data acquisition, processing and archival. Such
systems would be rendered worthless without the final piece of the puzzle: a system to allow user
access to the data archives.
There are many challenges concerning the future processing of satellite observations for
the purpose of quality assurance, long-term archiving, dissemination to users and overall
stewardship. Perhaps the largest involves the sheer volume of the data, which is becoming
available today and will continue in abundance from the proposed high-resolution sensors during
the next decade. The increasing data volumes can be clearly understood by the realization that
today an operational polar-orbiting satellite produces approximately 2 Terabytes worth of archival
data in one year, but within the next 10 years, 1 Terabyte of data per day will be common. The
movement of such large digital files through the Internet will require great increases in
communications band width, accompanied by similar increases in storage capacity for archivists
and users and in computer power and algorithms to allow for necessary data processing.
But regardless of size of the data bases, there are certain principles of stewardship of
data that must be followed to ensure that the data will be maintained in perpetuity and also
available to and usable by current and future users:
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Development and maintenance of an archive architecture and paradigm;
Efficient and effective operational acquisition and recovery of data and metadata;
Processing of data to ensure integrity;
Production and maintenance of inventories, directories, catalogues and other suitable
means to describe data and information;
Development and maintenance of a user access system and explicit documentation;
Long-term Preservation of data.
These principles will be described in some detail below.
Development and maintenance of archive architecture and paradigm
An archive architecture and paradigm must be established to allow for long-term
stewardship of large volume data sets and products. The overall paradigm is that the archive is in
a near-on-line status, with portions of it available on line for direct access by users. Near-line
archives will be able to deliver data to customers in a matter of minutes to hours, depending on the
volume of the request. The last piece of the long-term archive involves an off-site disaster backup
copy. For smaller volume data sets it may be possible to keep an additional copy, however for
large data sets this is prohibitively expensive.
Satellite data are recorded to digital archive media in units of data sets. Each dataset
carries a unique name, used as the tag for tracking and managing data in the archive. The Data
ANNEX III, p. 2
Set Catalogue contains one record for each data set. Each catalogue record carries the unique
data set name plus information about the physical location of a data set (e.g., the physical media
volume number and/or pathname). This one-to-one relationship of catalogue records to archive
data sets provides the essential link between archive and catalogue for managing the data archive.
Each catalogue record contains other data including digital signature information that is
necessary to ensure the integrity of data sets. This information is compared to freshly computed
digital signature information each time a data set is read, copied or moved. Any change indicates
a corrupted data set, which can be replaced from a back-up copy in the official archive.
Ancillary data sets, such as digital elevation models are also archived along with the
sensor data.
Metadata files carry documentation about data sources, the sensors, processing
algorithms and other information that are essential for efficient and effective use of the data. A
metadata file normally describes one type of data, for example from one sensor, and, therefore,
applies to numerous data sets. Metadata standards have been developed (for example by the
U.S. Federal Geographic Data Committee (FGDC-STD-001-1998) and should be followed in
creating and managing metadata.
Efficient and Effective Operational Acquisition and Recovery of data and metadata
The increasing volume of satellite data (e.g., IJPS, EOS, NPOESS, Next Generation
Geostationary Satellites…) will continually put pressure on the capacities of communications
system to move the data from points of receipt (ingest) to points of processing. The integrity of
data passing through these systems must be monitored and re-communications requests must be
issued as soon as possible, or the system will begin to fall behind and face difficult catch-up
scenarios as the data keeps pouring from the sensors. At this stage of processing quality
assurance procedures must be in place to ensure that the data received at the archive location is
the same as sent by the ingest facility. If there is a problem the data stream must be retransmitted.
Processing of data to ensure integrity
Once the data have arrived at an archival or data management centre, the continual
practice of checking for data integrity begins. Data and products carry digital signatures that
include data integrity parameters. These numbers are compared to freshly computed values at
each step of the archive and access process to ensure that data have not been lost or corrupted.
When data first arrive for archiving, the content of electronic transfers are validated against the
transfer log for completeness and tested for integrity via the digital signature. Reports on the
integrity of newly archived data and products are always available, and electronic notification of
problem data sets are made immediately. Other processing techniques should be in place after the
data have been received and checked for completeness. Periodic checks of the archive should be
performed to ensure that the integrity has not been damaged. But even more important is using
the data to create products or as supplements to operational activities that will help ensure its
integrity. This science-based quality assurance integrates product (operational or research)
processing into the quality assurance process.
In order to better address user needs for more efficiently determine what products are
useful for a given application, particularly long-term climate studies, archive centres will be
providing additional quality control information as well as expertise in the use of satellite data. This
additional information will include radiance diagrams (so-called radgrams), similar to those used
operationally by the numerical weather prediction centres, to monitor the time evolution of statistics
of individual satellite channels. Periodic re-analysis of data sets will occur to ensure the optimal
merging of all available data and to allow the production of retrieved geophysical variables with the
ANNEX III, p. 3
latest algorithms. The periodic re-analysis will also provide an opportunity to work with user groups
to access to entire data sets for new product development as the data sets are rotated from nearline archives to on-line archives for some limited period of time.
Production and maintenance of inventories, directories, catalogues and other suitable
means to describe data and information
Metadata, or information about the data, products and storage system and facilities are
vital components in the successful stewardship of data This information should be created, kept
up-to-date and stored in appropriate metadata catalogues, according to established principles.
The catalogues will be available to customers for online searches via the World Wide Web.
Metadata also serve the function of documenting the types of measurements made, sensor
characteristics, processing methodologies and other information that is important for users of the
data.
Data set naming conventions should be established to allow for clear, secure access to
appropriate data and products from multiple satellites and sensors. The digital signature
information and alternate data set name for each element should be stored as fields in the
inventory database for subsequent retrieval and distribution. Inventories will contain information
about the dates and geographic locations of each retrievable archive element, as well as its unique
identifier, and pointers to the physical media on which it is recorded. Inventories should be made
easily searchable via the Internet or other telecommunications mechanism. A browse facility
provides thumbnail images for user browsing and making decisions on which data sets to select.
Development and maintenance of a user access system and documentation
Customers for satellite data should be able to query an online metadata system to retrieve
information about the measurements available, the sensor characteristics, the data processing
status, data format, and other historical information about the data. Customers should also be able
to order data for delivery via FTP for smaller volumes of data. In the case of very large data
volumes, delivery via other media such as magnetic tape is preferred.
On-line data orders interface with customer service systems developed by the data
archivists. The access interface performs authorized searches, identifies data sampling
and delivery methods, and initiates data retrieval. Typically a customer may be able to
search by selecting:
(1) Satellite ID
(2) Data Type
(3) Channel
(4) Latitude / Longitude range
(5) Date / Time
(6) Correlation with other data, etc.
In some cases the customer may wish to establish a subscription service through which a
pre-determined class of data are “pushed” from the archive centre without customer interaction.
Elements from archive near-line mass storage are retrieved using a data base
management system. The integrity of the elements retrieved is validated via digital signature
information from the inventory database. To service requests for custom data sets (e.g., subsets),
the retrieval system processes data and creates new data set names and digital signatures.
ANNEX III, p. 4
Acquired data, associated metadata, and digital signature files are packaged for delivery
in a predefined order and format. If the data requested is not on-line and electronic transfer (FTP)
is selected as the delivery method in the Web-Internet access system, the requestor receives a
“data ready” notification via email. The notification will describe the procedures and cost for
retrieving the requested data.
For a robust analysis of long time series of data, for example when studying climate
variability and change, there will be a need for the periodic re-processing of data and analyses.
The reprocessing involves the use of standardized data assimilation algorithms for product
development and also corrects biases which may have crept into data over time due to the use of
different satellite sensors, orbital decay, etc. The complete archive of data and metadata must be
available at the re-processing site. Plans for the long-term archival of large volumes of satellite
data must take into account the requirements for data availability levied by reprocessing.
Long-term Preservation of data
Long-term preservation of satellite data is an on-going process which should involve
users, scientists, data managers, and data collectors. Knowledge of the data being preserved and
how it will be used are essential underpinnings for successful preservation. Procedures must be
developed to determine what data should be added or removed from the archive and when that
should be done. Detailed documentation of the data and derived products is essential to minimize
user access and processing problems and maximize the value of the archive.
For years there was only one mechanism for storing satellite data retrospectively: on
computer-compatible magnetic tapes. But with the recent reduction in the price of computer disk
storage, data can be stored on random access disks. But, because of the high volume of satellite
data and associated products, keeping much of the data on-line, that is, directly accessible by the
users, is not possible. A short time period of data might be kept on-line, with the remainder of the
retrospective data stored off-line or near-on-line as a working archive. To preclude the archive
becoming inaccessible in case of storage management system failure, data readability will be
independent of any storage management system.
Validated data sets and associated metadata (to include quality measures, electronic
signatures and checksums) are archived individually as discrete elements or data sets. Information
about each data element is entered into inventory catalogue databases and controlled by archive
management system software. The physical location and media types used are transparent to the
users.
Preservation of data in the archive is an active process that includes these activities:
(1) Copy data to archive media
(2) Create and maintain an archive backup copy off-site
(3) Migrate archive to new media and systems (hardware, operating system, software) to
maintain technology compatibility and as a data refreshment process
(4) Monitor data integrity at all above steps as well as during retrievals from the archive.
In addition, random samples of data in the archive are routinely checked for completeness
and integrity. Data sets which have integrity problems are replaced by copies made from backup
archive sources.
A data migration plan should be developed and implemented that entails the use of
automated robotic storage technology.
Migration is automatically performed by storage
ANNEX III, p. 5
management software according to established parameters. Migration is performed when new,
cost effective media are available to avoid being forced by media or systems obsolescence. All
migrated copies should be capable of being read by other established IT technology.
Copies of all data preserved in the official archive should be stored at an off-site backup
facility with a minimum of 50km distance separation from the archive facility. Such data will
maintain the form and structure of the permanently stored archive data and are subject to retrieval
(from the off-site storage) only at times of emergency. Transmission of data to the backup facility
will occur on a routine, scheduled basis.
The offsite archive facility may simply maintain copies of the media only or have varying
degrees of sophistication ranging to a fully operational off-site facility that mirrors the working
archive facility.
Archive facilities should determine, in consultation with users, how long to store data in an
archive, should maintain a data disposition schedule, and dispose of data sets on
scheduled dates. However, it would not be unusual for the highest resolution data,
metadata and product processing algorithms to be kept in perpetuity.
ANNEX IV
NEAR TERM CONFIGURATION OF THE SPACE BASED COMPONENT OF THE GLOBAL
OBSERVING SYSTEM
POLAR ORBITING SATELLITES
Visible and Infrared Radiometers
The Advanced Very High Resolution Radiometer (AVHRR), flown in October 1978 on
TIROS N, measures radiation in five visible and IR windows at 1 km resolution. This will transition
to a more capable visible and infrared imager called the Visible Infrared Imaging Radiometer Suite
(VIIRS), when the NOAA satellites become the NPOESS series, starting with a demonstration
program in 2005, called the NPOESS Preparatory Project (NPP). VIIRS will be better calibrated
than the AVHRR, have higher spatial resolution (400 meters versus 1 km at nadir), and have 22
spectral bands (between 0.4 and 12.5 µm) with additional capability for sea surface temperature,
aerosols, snow cover, cloud cover, surface albedo, vegetation index, sea ice, and ocean colour.
The Visible Infrared Scanner (VIRS) on TRMM is measuring radiances in 5 spectral bands
(0.63, 1.6, 3.75, 10.8, 12 µm) at 2 km nadir resolution. These data are being used in combination
with TRMM Microwave Imager (TMI) data to estimate precipitation locations and amounts.
NASA’s Moderate-Resolution Imaging Spectro-radiometers (MODIS); one on Terra and
another on Aqua, is designed to measure biological and physical processes on a global basis
every 1-to-2 days.
This multidisciplinary instrument is yielding simultaneous, congruent
observations of atmospheric (aerosol and cloud properties, water vapour and temperature profiles),
oceanic (sea-surface temperature and chlorophyll), and land-surface features (land-cover changes,
land-surface temperature, snow cover, and vegetation properties). The MODIS instrument
provides imagery in 36 discrete bands between 0.4 and 14.5 µm at 1 km resolution at nadir
(selected bands have 250 or 500 meter resolution); signal-to-noise ratios are greater than 500 at 1km resolution and absolute irradiance accuracies are < ±5% from 0.4 to 3 µm (2% relative to the
sun) and 1 percent or better in the thermal infrared (3.7 to 14.5 µm). Any point on the Earth is
covered at least once every 2 days.
On the Terra platform, the Advanced Thermal Emission and Reflection Radiometer
(ASTER), provided by the Japanese, is imaging the earth at very high-spatial-resolution (15- to 90m) in order to better understand the physical processes that affect climate change. ASTER
measures in three visible and near-infrared (VNIR) channels between 0.5 and 0.9 µm, with 15-m
resolution; six short-wave infrared (SWIR) channels between 1.6 and 2.43 µm, with 30-m
resolution; and five thermal infrared (TIR) channels between 8 and 12 µm, with 90-m resolution.
Combinations of VNIR, SWIR, and TIR are exploited for cloud studies, surface feature mapping,
soil and geologic studies, volcano monitoring, and surface temperature, emissivity, and reflectivity
determination.
The Along Track Scanning Radiometer (ATSR), flown on ERS-1 and –2 in the 1990’s, has
been providing multi-view multi-spectral measurements enabling very accurate determinations of
sea surface temperature. This enhancement to the operational AVHRR has been embraced by the
ocean community. The Advanced ATSR (AATSR), to be launched on ENVISAT in 2002, will
expand upon these capabilities thereby ensuring the production of a unique 10 year nearcontinuous data set at the levels of accuracy required (0.3 K or better) by both the operational and
climate communities.
The Ocean Color and Temperature Scanner (OCTS), onboard ADEOS launched in 1996,
achieved measurements in 8 visible and 4 infrared bands at 700 m nadir resolution applicable to
observation of the coastal zone and land. OCTS will be succeeded by the Global Imager (GLI) on
ANNEX IV, p. 2
ADEOS-2 in 2002. This optical sensor observes reflected solar light as well as emitted infrared
radiation from the Earth's surface including land, ocean and cloud. GLI data will be used to
provide global information such as surface temperature, vegetation distribution, and ice
distribution. These data may be used to determine the global circulation of carbon, to monitor
cloud, snow, ice and sea surface temperature, and to determine primary marine production. GLI
has 23 channels in visible and near-infrared region (VNIR), 6 channels in short wavelength infrared
region (SWIR), and 7 channels in middle and thermal infrared region (MTIR) for its multi spectral
observation. It has six atmospheric windows at 1.6, 2.2, 3.7, 8.6, 10.8, 12.0 micron and 3 water
vapour sensitive bands at 6.7, 7.3 and 7.5 micron. The ground resolution is 1km at nadir, but
some channels in VNIR and SWIR have a resolution of 250m.
China is evolving to FY-3 in 2004, their second series of polar orbiting meteorological
satellites. FY-3 will include Visible and Infrared Radiometer (VIRR) with ten visible and infrared
channels and the Moderate Resolution Visible and Infrared Imager (MODI) with 20 channels
located mainly at VIS and NIR region that are complementary to the VIRR.
Atmospheric Temperature and Humidity Sounders
In 1969 and 1970, two important instruments that paved the way for atmospheric
thermodynamic profiling were demonstrated in NASA’s NIMBUS program. The Satellite
Infrared Spectrometer (SIRS) and Infrared Interferometer Spectrometer (IRIS) paved the
way for multi-channel infrared radiometers and interferometers that could be used to
sound the atmosphere. An important operational milestone occurred in the remote
sounding of vertical temperature and humidity profiles in the atmosphere on a worldwide
basis with the TIROS Operational Vertical Sounder (TOVS). TOVS evolved to an
advanced version in 1998 that consists of the High resolution Infrared Radiation Sounder
(HIRS) and the Advanced Microwave Sounding Unit (AMSU). These 18 channel IR (3.7
to 14.7 µm) and 20 channel microwave (23.8 to 89 GHz) sounders produce temperature
and moisture profiles in clear and cloudy (non-precipitating) skies every fifty kilometres.
NOAA will be transitioning to more capable sounders in the NPOESS era, starting with a
demonstration program in 2005, called the NPOESS Preparatory Project (NPP). HIRS
will be replaced by the Cross Track Infrared Sounder (CrIS), a Michelson interferometer
measuring radiances from 4 to 15.4 µm at 2.5 to 0.6 wavenumber spectral resolution
respectively. CrIS is designed to enable retrievals of atmospheric temperature profiles at
1 degree accuracy for 1 km layers in the troposphere, and moisture profiles accurate to 15
percent for 2 km layers. The microwave sounder that will compliment CrIS in the
NPP/NPOESS era is the 22 channel (23.8 to 183.3 GHz) Advanced Technology
Microwave Sounder (ATMS), the next generation cross track microwave sounder.
The EUMETSAT METOP series will realize comparable sounding capability with the
Infrared Atmospheric Sounding Interferometer (IASI) in conjunction with the advanced microwave
temperature sounding units (AMSU-A) and microwave humidity sounders (MHS / HSB). IASI is a
Michelson interferometer measuring radiances from 3.5 to 16.6 µm at 0.25 wavenumber spectral
resolution. CrIS/ATMS will fly in the afternoon (1330 ascending) and IASI/AMSU/MHS will fly in
the morning (0930 descending) orbit.
The Russian Federation is expanding their Meteor-3M with the launch of N1 in late 2001
and N2 in 2004. N1 carries the sounding microwave radiometer MTVZA (20 channels in the range
of 18 to 183 GHz). This will be complemented with an infrared sounding interferometer on N2.
China is adding to their FY-3 series the Infrared Atmospheric Sounder (IRAS). IRAS will
have 26 channels where the first 20 channels are almost the same as HIRS/3 and remaining six
channels designed to measure aerosols, stratosphere temperature, carbon dioxide content and
cirrus. IRAS will be complemented by an 8 channel Microwave Atmospheric Sounder (MWAS) for
temperature sounding in cloudy areas.
ANNEX IV, p. 3
India has also realized microwave observations for meteorology and oceanographic studies with
OCENSAT-1 launched in May 1999 with Multichannel Scanning Microwave radiometer (MSMR)
along with Ocean Colour Monitor (OCM).
Microwave All-Weather Radiometers
Since June 1987, a series of DMSP satellites in polar orbit have carried a scanning
microwave radiometer called the Special Sensor Microwave Imager (SSM/I). Because of the
ability of microwave radiation to penetrate clouds, SSMI provides night-day, all-weather imaging of
the land and ocean surface. NOAA has used the DMSP SSM/I data extensively. A conical
scanning microwave imager/sounder will be flown on NPOESS. The Conically Scanning
Microwave Image / Sounder (CMIS) will combine the microwave imaging capabilities of Japan’s
Advanced Microwave Scanning Radiometer (AMSR) on EOS PM-1, and the atmospheric sounding
capabilities of the Special Sensor Microwave Imager / Sounder (SSMI/S) on the current DMSP
satellites. Polarization for selected imaging channels (vertical, horizontal, and +/- 45 degrees) will
be utilized to derive ocean surface wind vectors similar to what has previously been achieved with
active scatterometers. Although demonstrated on airborne platforms, space based validation of
the passive microwave technique for wind vector derivation will await the Windsat Coriolis mission
in late 2001. CMIS data can be utilized to derive a variety of parameters for operations and
research including all weather sea surface temperature, surface wetness, precipitation, cloud liquid
water, cloud base height, snow water equivalent, surface winds, atmospheric vertical moisture
profile, and atmospheric vertical temperature profile.
Since 1997, NASA has been flying the TRMM Microwave Imager. This multi-channel
(10.7, 19.4, 21.3, 37, and 85.5 GHz) dual polarized radiometer is providing measurements useful
for estimating rainfall rates over oceans. When used in combination with on-board Precipitation
Radar (PR) and VIRS data, precipitation profiles can be estimated.
NASDA has developed the AMSR for high precision monitoring of the global circulation of
water and energy cycles regardless of cloud cover. AMSR has 8 frequency bands from 6.9GHz to
89GHz with spatial resolution of about 5km in the 89GHz band and about 60km in the 6.9GHz
band. AMSR is able to observe water vapour, precipitation and cloud liquid water and ice like
previous microwave radiometers but with much higher spatial resolution. It will fly on Aqua and
ADEOS-2 in 2002.
ESA has been flying microwave radiometers (MWR) that measure the integrated
atmospheric water vapour column and cloud liquid water content, as correction terms for the radar
altimeter signal. In addition, MWR measurement data are useful for the determination of surface
emissivity and soil moisture over land, for surface energy budget investigations to support
atmospheric studies, and for ice characterization. The next MWR, to be flown on ENVISAT, has
evolved from the instruments previously flown on ERS-1 and ERS-2.
China is adding to their FY-3 series the conically scanning Microwave Radiation Imager
(MWRI) with 12 channels. This sensor measures thermal microwave radiation from land and
ocean surfaces, as well as being sensitive to various forms of water and moisture in the
atmosphere, clouds and surfaces. These channels can penetrate clouds and provide forecasters
with an all weather measurement capability. At higher frequency channels, the scattering
signatures from the cloud and precipitation are also good for detecting rainfall.
Multi-angle Imagers
France developed the Polarization and Directionality of Earth Reflectance (POLDER)
instrument to measure the polarization, and directionality and spectral characteristics of the solar
light reflected by aerosols, clouds, oceans, and land surfaces. It flew on ADEOS (1996) and will
again be on ADEOS-2 (2002). POLDER is a 2 dimensional CCD array, with wide field of view,
ANNEX IV, p. 4
multi band imaging radiometer and polarimeter developed by CNES. Multi-angle viewing is
achieved at quasi constant 7km × 6km resolution; a filter and polarizer wheel that rotates and
scans eight narrow spectral bands in the visible and near infrared (443, 490, 564, 670, 763, 765,
865, 910 nm), and three polarization angles at 443, 670 and 865 nm. POLDER is intended to
determine the influence of aerosols and clouds on the Earth's Radiation Budget and to quantify the
role of photosynthesis from the continental and oceanic biosphere in the global carbon cycle.
The Multi-angle Imaging Spectro-Radiometer (MISR) flying on Terra is providing multipleangle, continuous sunlight coverage of the Earth with high spatial resolution. MISR captures
multidirectional observations of each scene within a time scale of a few minutes, thereby under
virtually the same atmospheric conditions. MISR uses nine individual charge-coupled device
(CCD) based pushbroom cameras to observe the Earth at nine discrete view angles: one at nadir,
plus eight other symmetrical views at 26.1, 45.6, 60.0, and 70.5° forward and aftward of nadir.
Images at each angle are obtained in four spectral bands centred at 446, 558, 672, and 866 nm
sampling at 275 m, 550 m, or 1.1 km. Global multi-angle coverage of the entire Earth is
accomplished in 9 days at the equator, and 2 days at the poles. MISR measures the heterogeneity
and altitude of clouds, aerosols, and the earth surface.
Ozone Monitors
One approach used to derive vertical profiles of ozone utilizes the ultraviolet portion of the
electromagnetic spectrum. The Solar Backscatter Ultraviolet (SBUV), which provides information
on ozone amounts for atmospheric 7 to 10 km layers, was incorporated into the operational series
of NOAA polar satellites (POES) beginning with NOAA-9 in 1984. The Total Ozone Mapping
Spectrometer (TOMS) flew on Nimbus-7 and provided critical image data that confirmed the
existence of the Antarctic ozone hole. The Nimbus-7 TOMS lasted into the 1990s and was
replaced subsequently by TOMS sensors flying on a Russian Meteor spacecraft, the Japanese
ADEOS, and a NASA Earth Probe. The TOMS equivalent capability will be continued with the
flight of the Dutch provided Ozone Mapping Instrument (OMI) on NASA’s Chemistry mission in
2002 and subsequently the Ozone Mapping and Profiler Suite (OMPS) on NPOESS, being
developed for flight on afternoon (1330 ascending) NPOESS platforms. It consists of a nadir
scanning ozone mapper similar in functionality to TOMS and a limb scanning radiometer that will
be able to provide ozone profiles with vertical resolution of 3 km. Depending upon its ultimate
design, the OMPS may be able to provide some of the same capability as limb scanning sensors
on NASA’s UARS and EOS Chem. However in the near term, there is concern about a possible
gap in TOMS type data coverage.
The first European ozone monitoring instrument, ESA’s Global Ozone Measurement
Experiment, GOME, flew onboard the ERS-1 satellite in the 1990s. The primary objective of
GOME was the measurement of total column amounts and profiles of ozone and of other gases
involved in ozone photo chemistry. The Global Ozone Monitoring by Occultation of Stars
(GOMOS) on board ENVISAT (to be launched in 2002) is the newest ESA instrument aiming at
ozone monitoring. GOMOS measurements are based on star occultations and offer significant
advantages in terms of good/uniform global coverage, day- and night-side operation, and high
vertical resolution. It is intended to provide altitude-resolved global ozone mapping and trend
monitoring with very high accuracy, as needed for the understanding of ozone chemistry and for
model validation. The primary GOMOS mission objectives are: (a) measurement of profiles of
ozone, NO2, NO3, OClO, temperature, and water vapour; (b) day- and night-side measurement
capability; (c) global coverage with typically over 600 profile measurements per day; (d) altitude
measurement capability between the tropopause and 100 km; and (e) altitude resolution of better
than 1.7 km.
Japan is launching an Improved Limb Atmospheric Sounder (ILAS-II) on ADEOS-2 in
2002. That sensor is intended to monitor and study changes in the stratosphere that are triggered
by emissions of chlorofluorocarbon (CFC), and to evaluate the effectiveness of world wide
ANNEX IV, p. 5
emission controls of CFC. ILAS-II is a spectrometer that observes the atmospheric limb
absorption spectrum from the upper troposphere to the stratosphere using sunlight as a light
source (solar occultation technique). The spectrum is analyzed to provide information of height
distribution of atmospheric component density, temperature and pressure. The spectrometer
covers the infrared region (3-13 micrometer) and the near visible region (753 to 784nm). From
these spectral observations, ILAS-II can measure the vertical profiles of species related to ozone
depletion phenomena: ozone (O3), nitrogen dioxide (NO2), nitric acid(HNO3), aerosols, water
vapour (H2O), CFC-11, CFC-12, methane (CH), nitrous oxide (N2O), chlorine nitrate (ClONO2),
and temperature and pressure as well. ILAS-II improves upon the initial measurements made by
ILAS (aboard ADEOS in 1996).
China is also including on FY-3 a Total Ozone Mapper and Ozone Profiler (TOM/OP),
consisting of two instruments to measure ozone in the earth’s atmosphere. TOM is a 6-channel
spectrometer with 50km resolution at the nadir and OP is a 12-channel spectrograph with 200 km
resolution at nadir.
Atmospheric Chemistry Instruments
NASA with the Canadian Space Agency launched the Measurements Of Pollution In The
Troposphere (MOPITT) in Terra in 1999. MOPITT is measuring emitted and reflected infrared
radiance in the atmospheric column that permits retrieval of global and temporal distributions of
tropospheric CO profiles and total column CH4. Both CO and CH4 are produced by biomass
systems, oceans, and human activities. MOPITT operates on the principle of correlation
spectroscopy, i.e., spectral selection of radiation emission or absorption by a gas, using a sample
of the same gas as a filter. Atmospheric CO sounding and column CO and CH4 are mapped at 22
km by using thermal and reflected solar channels in the regions of 4.7 and 2.3 µm, respectively.
Scientific studies are employing these data to derive three-dimensional global maps as part of an
effort to model global tropospheric chemistry.
ESA will be flying the Michelson Interferometer for Passive Atmospheric Sounding
(MIPAS) on ENVISAT (in 2002). It is a Fourier transform spectrometer for the measurement of
high-resolution gaseous emission spectra at the Earth's limb. It operates in the near to mid
infrared where many of the atmospheric trace-gases playing a major role in atmospheric chemistry
have important emission features. The objectives of MIPAS are: (a) simultaneous and global
measurements of geophysical parameters in the middle atmosphere; (b) stratospheric chemistry of
O3, H2O, CH4, N2O, and HNO3; (c) climatology of temperature, CH4, N2O, and O3; (d) study of
chemical composition, dynamics, and radiation budget of the middle atmosphere; and (e)
monitoring of stratospheric O3 and CFC's.
The SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY
(SCIAMACHY) on ENVISAT will also be performing global measurements of trace gases in the
troposphere and in the stratosphere. It is a is a passive remote sensing spectrometer observing
backscattered, reflected, transmitted or emitted radiation from the atmosphere and Earth's surface,
in the wavelength range between 240 and 2380 nm.
Scatterometers
The first active radar scatterometer to determine wind speed and direction over the ocean
surface was flown on Seasat in 1978. Great progress in this area was possible in the 90’s with the
SCAT data on ERS-1 and –2. A NASA scatterometer termed NSCATT flew on Japan’s ADEOS
from August 1996 to June 1997; scientists were able to show a significant positive impact in
predicting marine forecasting, operational global numerical weather prediction, and climate
forecasting. A follow-on mission, Quickscat, launched in 1999, carries the NSCATT successor
instrument, SeaWinds. Another SeaWinds sensor is scheduled to fly on ADEOS-2 in 2002.
ANNEX IV, p. 6
SeaWinds provides high accuracy sea surface wind speed and direction measurements
by observing microwave reflection from the sea surface. Its wind speed accuracy is 2m/s, wind
direction accuracy is 20 degrees, and spatial resolution is 50km; it covers the globe every 2 days.
Analyzing SeaWinds' data, combined with those of AMSR or GLI will contribute to the analyses of
water circulations and other ocean phenomena.
No additional U.S. scatterometer missions are planned before NPOESS, which plans to
use a passive microwave approach with CMIS (Conically Scanning Microwave Image/Sounder) to
determine the ocean vector wind field. This passive microwave technique will be tested as part of
the Windsat Coriolis mission scheduled for late 2001. Europe’s METOP series of satellites,
scheduled to begin flying in late 2005 include an Advanced Scatterometer (ASCAT) sensor, will
enable continuous measurements from a polar orbit through 2020. Tandem missions of broad
swath instruments are being discussed to enhance sampling of surface wind fields for
oceanographic use; one possibility is for NASDA to fly a NASA scatterometer on GCOM-B1 and
the missions of QuikScat and SeaWinds on NASDA’s ADEOS-2 to be extended as long as
possible.
Ocean Colour Instruments
Ocean colour measurements are important in four broad domains of applications; they are
the ocean carbon cycle, the thermal regime of the upper ocean, the management of fisheries, and
the management of coastal zones. The first observations of ocean colour from space were carried
out by the experimental Coastal Zone Colour Scanner (CZCS) from 1978 to 1986 aboard NASA's
Nimbus-7 satellite. This instrument provided global and regional data sets which yielded a wealth
of new information about the distribution and seasonal variability of primary productivity. During
the period 1986 to 1996, the orderly development of ocean-colour science was hindered by the
lack of an operational satellite producing ocean-colour data. Although considerable energy was
devoted to analysing the results of the then-defunct Coastal Zone Colour Scanner, there was no
opportunity to conduct new studies in which observations from space could be matched with in situ
data observations in near real time.
The picture began to improve in March 1996, when India launched the German sensor
MOS. Although this device does not give global coverage, it is important as the first source of new
ocean-colour data in ten years. In August 1996, Japan launched the Japanese sensor OCTS and
the French sensor POLDER on the ADEOS mission. This was a very powerful combination, which
operated until June 1997. In August 1997, the USA launched the SeaWiFS sensor, which has
been in routine operation since September 1997. This instrument is now providing complete global
coverage of the oceans every two days.
A number of ocean colour instruments are planned for the very near future (GLI, MISR,
MODIS, MERIS, OCI, OCM, OSMI, POLDER-2 scheduled to be launched in 1999-2002). Recent
experience has emphasized that a certain controlled redundancy is essential to ensure an
unbroken stream of ocean-colour data into the indefinite future.
The MEdium Resolution Imaging Spectrometer (MERIS) instrument will be on ENVISAT
and will be the latest imaging spectrometer that measures the solar radiation reflected by the
Earth, at a ground spatial resolution of 300m, in 15 spectral bands, it’s visible and near infra-red
measurements provide global coverage of the Earth in 3 days. The MERIS mission is the
measurement of sea colour in the oceans and in coastal areas. Knowledge of the sea colour can
be converted into a measurement of chlorophyll pigment concentration, suspended sediment
concentration and of aerosol loads over the marine domain.
ANNEX IV, p. 7
Radiation Budget Monitors
The Earth’s radiation budget and atmospheric radiation from the top of the atmosphere to
the surface was first measured by the Earth Radiation Budget (ERB) sensors. They flew on
Nimbus in 1978, as well as on a free flyer and on NOAA-9 and -10 in the mid 1980s. The Clouds
and Earth Radiant Energy System (CERES) is continuing these broad band visible and infrared
measurements on the Tropical Rainfall Measuring Mission (TRMM) which was launched in
November 1997. CERES is also planned for the NPOESS orbit (1330 local time ascending orbit).
Two CERES scanners (one each working in the biaxial and cross track mode) will be in orbit with
EOS Terra in December 1999 and EOS Aqua in February 2002.
CERES data are providing accurate cloud and radiation flux measurements that are
fundamental inputs to models of oceanic and atmospheric energetics, and also contributes to
extended-range weather forecasting. CERES measures the radiative flows (0.3 – 5 µm, 8 – 12
µm, 0.3 – 50 µm) at the top of the atmosphere at 10 km nadir resolution, and combines these data
with that from higher resolution imagers (VIRS on TRMM, MODIS on Terra/Aqua) to produce cloud
properties and radiative fluxes through the atmosphere as well as the radiative energy budget at
the Earth's surface. The imagers allow determination of cloud top height, fractional cloud cover,
cloud liquid water path, droplet size, and other cloud properties that are consistent with the
radiative fluxes. CERES provides, for the first time, a critical tie between the measurements of the
radiation budget and synchronous measurements of cloud properties. CERES also is providing
improved measurements of clear-sky radiative fluxes, that near-simultaneous measurements of
both well-calibrated cloud-imager and broadband radiation measurements.
Altimeters
Altimeters measure the oceans’ topography which provides information on the ocean
current velocity, the sea level response to global warming/cooling and hydrological balance, the
marine geophysical processes (such as crustal deformation), and the global sea state. Altimeters
flew on the European ERS-1 and –2 satellites in the 1990’s and provided a major quasi-operational
contribution. JASON-1, a joint mission between France and the USA scheduled for 2002, will
monitor the global ocean with a solid state radar altimeter (POSEIDON-2), a Doppler tracking
system receiver (DORIS), a Microwave radiometer, a GPS tracking receiver, and a laser retroreflector array. A JASON-2 mission will follow-on to the JASON-1 mission with a launch in 2005.
NOAA is planning to manifest a dual frequency microwave radar altimeter for its morning (0530
descending) NPOESS platforms.
Positioning Sensors
Geometric determinations of location depend on inferences about the atmospheric
temperature and moisture concentrations; they provide valuable complementary information to
tropospheric infrared and microwave sounders about the tropopause and stratosphere. Ray
bending and changes in the phase and amplitude of the transmitted signals allowing inference of
the upper atmosphere temperature profile to the order of 1 deg K or better between altitudes of 8 to
30 km in layers (with footprints ranging between 1 km x 30 km to 1 km x 200 km extent) with near
global coverage. The coverage would be expected to be evenly spread over the globe, excepting
polar regions. The system measures upper atmospheric virtual temperature profiles so data from
the lower atmosphere would require alternate data to separate vapour pressure and temperature
traces.
The Global Positioning System Occultation Sensor (GPSOS) will measure the refraction
of radiowave signals from the GPS constellation and Russia’s Global Navigation Satellite System
(GLONASS). This uses occultation between the constellation of GPS satellite transmitters and
receivers on LEO satellites. The GPSOS will be used operationally for spacecraft navigation,
characterizing the ionosphere, and experimentally to determine tropospheric temperature and
ANNEX IV, p. 8
humidity. A similar system, GPSMET, flew in 1995. A GPS occultation system was recently
provided for launch on the Oersted / Sunsat mission and variations will also be included on
CHAMP, SAC-C and GRACE, all scheduled to be launched before 2002. NOAA is planning to
manifest a GPSOS on all NPOESS platforms.
The EUMETSAT Polar-orbiting Satellite (METOP), due to be launched in late 2005, will fly
a GPS Radio occultation Atmospheric Sounder (GRAS). GRAS will provide ‘all weather’
temperature profiles with high vertical resolution in the upper troposphere and stratosphere, and
humidity profiles in the lower troposphere.
A promising research GPS system is WATS (Water Vapour and Temperature in the
Troposphere and Stratosphere) where ESA plans to provide accurate dense, stable mapping of
atmospheric humidity by means of a constellation of six micro-satellites (proposed as an ESA
Earth Explorer Core Mission) that exploit GNSS occultation signals and occultation of signals in Xand Ka band between the micro-satellites. Intended for 2008 launch, WATS will provide about
6000 daily profiles of key ionospheric and atmospheric properties from the tracked GPS radiosignals as they are occulted behind the Earth limb. ESA is also maintaining ACE (Atmospheric
Climate Experiment), consisting of a constellation of micro-satellites with GNSS atmospheric
sounding receivers, as a hot stand-by mission.
Wind Lidars
ESA is preparing the Atmospheric Dynamics Mission (ADM-Aeolus) for launch in 2007 on
their Earth Explorer series. ADM-Aeolus will provide improved analyses of the global wind field in
three-dimensions. This will correct the major deficiency in current wind-profiling techniques and
also advance the techniques of atmospheric modelling and analysis. ADM-Aeolus will utilise active
Doppler Wind Lidars (DWL) to provide the wind profiles globally, from direct wind observations, as
well as cloud top heights, vertical distribution of clouds, aerosol properties, and wind variability. It
is expected that ADM-Aeolus, will have a great effect upon operational weather forecasting and
enable a more detailed study of the balance and circulation of wind energy on Earth. ADM is a
single angle viewing lidar and the derivation of winds will require use of NWP.
Hyperspectral Imagers
Although not designed for atmospheric purposes, NASA’s Earth Observing (EO-1)
mission launched in November 2000 carries two instruments of great interest to the atmospheric
science community. They are the Hyperion, a narrow swath (7.5 km) hyperspectral instrument with
30 meter horizontal resolution that samples the spectrum between 0.4 and 2.4 m at 10 nanometer
resolution, and the Local Atmospheric Corrector (LAC), a wide swath (185 km) hyperspectral
instrument with 250 meter horizontal resolution that samples the spectrum between 0.9 m and 1.6
m at 2-6 nanometer spectral resolution. Both Hyperion and LAC scan across important water
vapour absorption bands, and should be able to provide information on the atmosphere’s
horizontal water vapour distribution at resolutions here-to-fore unattainable. This important
information will help define the requirements for design of future satellite and in situ water vapour
measuring systems.
GEOSTATIONARY SATELLITES
Visible and Infrared Radiometers
The Visible and Infrared Spin Scan Radiometer (VISSR), flown since 1974, has been the
mainstay of geostationary imaging on GOES, Meteosat, FY-2 and GMS. VISSR enables 5 to 7 km
resolution images of the full earth disk every 30 minutes in 2 or 3 visible and infrared windows and
one water vapour sensitive band. The USA changed to a staring imager with 5 channels of visible
ANNEX IV, p. 9
and infrared measurements (0.6, 3.9, 6.7, 11, 12 µm) at 5 km resolution with full disk coverage in
30 minutes in 1993. More changes are underway.
Europe is moving to the Spinning Environmental Visible and Infrared Instrument (SEVIRI)
with 12 channels of visible and infrared measurements (broad band visible, 0.6, 0.8, 1.6, 3.9, 6.3,
7.4, 8.7, 9.7, 10.8, 12, 13.4 µm) at 3 km resolution full disk every 15 minutes. For meteorological
applications, the following channels are of particular interest: (a) 13.4 µm (CO2) plus 6.3 and 7.4
µm (H2O) channels will improve the height assignment of clouds and provide information on the
vertical moisture distribution in the atmosphere; (b) 3.9 µm allows for low cloud and fog detection;
(c) split window (10.8 µm and 12.0 µm) together with 3.9 µm have a good potential for land and
sea surface temperature determination; and (e) 1.6 µm and 8.7 µm enable discrimination between
ice and water clouds. A particular advantage of SEVIRI data for land applications is better
monitoring of diurnal variations of cloudiness, earth surface temperature or solar irradiation at the
Earth's surface.
Japan will embark on the MTSAT with 5 channels of visible and infrared measurements
(0.6, 4, 6.7, 11, 12 µm) at 5 km resolution full disk every 30 minutes. China has the FY 2 series of
imagers that will be adding several spectral channels to their current visible and infrared window
measurements.
USA will evolve to an Advanced Baseline Imager (ABI) that makes full disk images in 8 to
12 spectral bands (similar to those on SEVIRI) in 5 minutes at 2 km infrared and 0.5 km visible
resolution. ABI offers improved performance over current GOES in all dimensions (routine full
Earth disk imaging while enabling mesoscale sub one minute interval imaging, better navigation,
more noise free signals, and additional spectral bands for improved moisture feature detection).
India has been supporting their INSAT series of satellites (with VIS, IR channels) since
1984 for operational short, medium range and synoptic forecasts. INSAT-2E with water vapour
channel imaging has been useful for monitoring and forecasting monsoon and cyclone
development; INSAT-2E also has a CCD camera providing 1km spatial resolution with around
600km swath. INSAT-3A and Metsat, to be launched by 2002, will have similar capabilities to
image in 3 bands. INSAT-3D (to be launched in 2004) will include a split window thermal channel
enabling continuous SST monitoring.
China plans to continue evolving their five planned FY-2 geosynchronous satellites. FY2C, to be launched in 2004, will improve on the first two experimental satellites FY-2A and 2B. The
number of spectral channels of Visible and Infrared Spin Scan Radiometer (VISSR) will be
increased from 3 to 5.
Infrared Sounding
With the three axis stablized platform on GOES-8, NOAA was able to introduce
geostationary infrared sounders. Measuring the infrared radiation in 18 spectral bands (between
3.7 and 14.7 µm), these sounders provide temperature and moisture sounding over North America
and nearby oceans every hour every 30 km (in clear skies). A variety of products and applications
are described in the literature (Menzel et al., 1998). NOAA plans to evolve to the Advanced
Baseline Sounder (ABS) in 2011, using an interferometer, focal plane detector arrays, and on
board data processing to cover 4.4 to 15.4 µm with 2000 plus channels measuring radiation from 5
km resolution; contiguous coverage of 6000 by 5000 km will be accomplished in less than 60
minutes. NASA will be demonstrating the technology necessary for ABS, with the Geostationary
Imaging Fourier Transform Spectrometer (GIFTS) in 2005. GIFTS will improve observation of all
three basic atmospheric state variables (temperature, moisture, and wind velocity) with much
higher spatial, vertical, and temporal resolutions. Water vapour, cloud, and trace gas features will
be used as tracers of atmospheric transport. GIFTS observations will improve measurement of the
atmospheric water cycle processes and the transport of greenhouse and pollutant gases. GIFTS
ANNEX IV, p. 10
and ABS represent a significant advance in geostationary sounding capabilities and brings
temporal and horizontal and vertical sounding resolutions into balance for the first time ever.
India will be adding a sounding capability to INSAT-3D in 2004; the planned 19 channel
sounder will be able to estimate the temperature and humidity profiles of the atmosphere in order
to study the mesoscale phenomena in tropical latitudes.
Radiation Budget
EUMETSAT will be flying the Geostationary Earth Radiation Budget (GERB) starting on
Meteosat Second Generation in 2002. This will enable hourly measurements of the Earth’s
radiation budget and atmospheric radiation from the top of the atmosphere to the surface. The
GERB payload will be a scanning radiometer with two broadband channels, one covering the solar
spectrum, the other covering the entire electromagnetic spectrum. Data will be calibrated on board
in order to support the retrieval of radiative fluxes of reflected solar radiation and emitted thermal
radiation at the top of the atmosphere with an accuracy of 1%.